Where to open a new coffee shop/Where to open a new coffee shop.ipynb

{"cells":[{"source":"# Where to open a new coffee shop?\n\n## 📖 Background\nYou are helping a client who owns coffee shops in Colorado. The company's coffee shops serve high-quality and responsibly sourced coffee,  pastries, and sandwiches. They operate three locations in Fort Collins and want to expand into Denver. \n\nYour client believes that the ideal location for a new store is close to affluent households, and the store appeals to the 20-35 year old demographic.\n \nYour team collected geographical and demographic information about Denver's neighborhoods to assist the search. They also collected data for Starbucks stores in Denver. Starbucks and the new coffee shops do not compete for the same clients; the team included their location as a reference.","metadata":{},"id":"9a9ada08-c9f0-466f-91a0-265fa539f9f5","cell_type":"markdown"},{"source":"## 💾 The data\nYou have assembled information from three different sources ([locations](https://github.com/chrismeller/), [neighborhoods](http://data.denvergov.org), [demographics](https://www.census.gov/)):\n\n#### Starbucks locations in Denver, Colorado\n- \"StoreNumber\" - Store Number as assigned by Starbucks\n- \"Name\" - Name identifier for the store\n- \"PhoneNumber\" - Phone number for the store\n- \"Street 1, 2, and 3\" - Address for the store\n- \"PostalCode\" - Zip code of the store\n- \"Longitude, Latitude\" - Coordinates of the store\n\n#### Neighborhoods' geographical information\n- \"NBHD_ID\" - Neighborhood ID (matches the census information)\n- \"NBHD_NAME\" - Name of the statistical neighborhood\n- \"Geometry\" - Polygon that defines the neighborhood\n\n#### Demographic information\n- \"NBHD_ID\" - Neighborhood ID (matches the geographical information)\n- \"NBHD_NAME' - Nieghborhood name\n- \"POPULATION_2010' - Population in 2010\n- \"AGE_ \" - Number of people in each age bracket (< 18, 18-34, 35-65, and > 65)\n- \"NUM_HOUSEHOLDS\" - Number of households in the neighborhood\n- \"FAMILIES\" - Number of families in the neighborhood\n- \"NUM_HHLD_100K+\" - Number of households with income above 100 thousand USD per year\n\n_Starbucks locations were scrapped from the Starbucks store locator webpage by [Chris Meller](https://github.com/chrismeller/)._  \n_Statistical Neighborhood information from the [City of Denver Open Data Catalog](http://data.denvergov.org), [CC BY 3.0](http://creativecommons.org/licenses/by/3.0/) license._      \n_Census information from the [United States Census Bureau](https://www.census.gov/). Publicly available information._","metadata":{"tags":[]},"id":"10dcc269-3659-4851-99cd-f1ffb7f818aa","cell_type":"markdown"},{"source":"%%capture\npip install geopandas","metadata":{},"id":"20fc09c0-2543-4223-8dd8-6b5c529b01c6","cell_type":"code","execution_count":128,"outputs":[]},{"source":"import pandas as pd\nimport geopandas as gpd\ndenver = pd.read_csv('./data/denver.csv')\ndenver","metadata":{},"id":"40251159-3eea-47f8-bfb4-09e38d5ced65","cell_type":"code","execution_count":129,"outputs":[{"output_type":"execute_result","execution_count":129,"data":{"application/com.datacamp.data-table.v1+json":{"table":{"schema":{"fields":[{"name":"index","type":"integer"},{"name":"StoreNumber","type":"string"},{"name":"Name","type":"string"},{"name":"PhoneNumber","type":"string"},{"name":"Street1","type":"string"},{"name":"Street2","type":"string"},{"name":"Street3","type":"string"},{"name":"PostalCode","type":"integer"},{"name":"Longitude","type":"number"},{"name":"Latitude","type":"number"}],"primaryKey":["index"],"pandas_version":"1.4.0"},"data":[{"index":0,"StoreNumber":"27708-240564","Name":"Auraria Tivoli - Student Union","PhoneNumber":"3035563700","Street1":"900 Auraria Parkway","Street2":null,"Street3":null,"PostalCode":80204,"Longitude":-105.01,"Latitude":39.75},{"index":1,"StoreNumber":"75828-94910","Name":"Grand Hyatt Denver","PhoneNumber":"303-295-1234","Street1":"1740 Welton St","Street2":null,"Street3":null,"PostalCode":802023940,"Longitude":-104.99,"Latitude":39.75},{"index":2,"StoreNumber":"74105-23807","Name":"Safeway - Denver #1463","PhoneNumber":"303-722-6117","Street1":"2150 S Downing","Street2":null,"Street3":null,"PostalCode":802104528,"Longitude":-104.97,"Latitude":39.68},{"index":3,"StoreNumber":"22871-102491","Name":"Safeway - Denver #1248","PhoneNumber":"303-477-5091","Street1":"2660 Federal Blvd","Street2":null,"Street3":null,"PostalCode":80211,"Longitude":-105.02,"Latitude":39.76},{"index":4,"StoreNumber":"11105-100844","Name":"Highlands Garden Village","PhoneNumber":"303-455-3382","Street1":"4500 W 38th Ave","Street2":null,"Street3":null,"PostalCode":802122001,"Longitude":-105.05,"Latitude":39.77},{"index":5,"StoreNumber":"75003-33060","Name":"King Soopers-Denver #29","PhoneNumber":"303-832-5262","Street1":"1155 E. 9th Ave.","Street2":null,"Street3":"Capitol Hill Center","PostalCode":802184802,"Longitude":-104.97,"Latitude":39.73},{"index":6,"StoreNumber":"19695-165566","Name":"Hampden & Santa Fe, Sheridan","PhoneNumber":"3037611675","Street1":"3500 A River Point Parkway","Street2":null,"Street3":null,"PostalCode":80110,"Longitude":-105.01,"Latitude":39.65},{"index":7,"StoreNumber":"74108-17656","Name":"Safeway-Denver #1267","PhoneNumber":"303-691-8870","Street1":"6460 East Yale Ave","Street2":null,"Street3":null,"PostalCode":802227156,"Longitude":-104.91,"Latitude":39.67},{"index":8,"StoreNumber":"18946-177247","Name":"Target Denver T-2820","PhoneNumber":"303-481-2821","Street1":"7777 E. Hampden Ave","Street2":null,"Street3":null,"PostalCode":80231,"Longitude":-104.9,"Latitude":39.65},{"index":9,"StoreNumber":"5440-1319","Name":"5835 Leetsdale","PhoneNumber":"(303) 388-8367","Street1":"5835 Leetsdale Drive","Street2":null,"Street3":null,"PostalCode":802241222,"Longitude":-104.92,"Latitude":39.71},{"index":10,"StoreNumber":"6205-30","Name":"UMB Bank Plaza","PhoneNumber":"(303) 863-1826","Street1":"1670 Broadway","Street2":null,"Street3":null,"PostalCode":802024801,"Longitude":-104.99,"Latitude":39.74},{"index":11,"StoreNumber":"6618-69880","Name":"Santa Fe & Mississippi","PhoneNumber":"303-715-0600","Street1":"1285 S Santa Fe Drive","Street2":null,"Street3":null,"PostalCode":802233214,"Longitude":-104.99,"Latitude":39.69},{"index":12,"StoreNumber":"10862-103208","Name":"Lowry Town Center - Denver","PhoneNumber":"303-340-5843","Street1":"200 Quebec St.","Street2":null,"Street3":null,"PostalCode":802307144,"Longitude":-104.9,"Latitude":39.72},{"index":13,"StoreNumber":"79292-99564","Name":"King Soopers - Denver #72","PhoneNumber":"303-512-0122","Street1":"2750 S Colorado Blvd","Street2":null,"Street3":null,"PostalCode":802226602,"Longitude":-104.94,"Latitude":39.67},{"index":14,"StoreNumber":"15466-156825","Name":"Embassy Suites Denver Downtown Conv","PhoneNumber":"303-592-1000","Street1":"1420 Stout St","Street2":null,"Street3":null,"PostalCode":802023201,"Longitude":-104.99,"Latitude":39.74},{"index":15,"StoreNumber":"74296-77462","Name":"Safeway-Denver #244","PhoneNumber":"303-458-8418","Street1":"3800 W 44th Ave","Street2":null,"Street3":null,"PostalCode":802111305,"Longitude":-105.04,"Latitude":39.78},{"index":16,"StoreNumber":"6342-10647","Name":"REI Denver","PhoneNumber":"303/477-5445","Street1":"1416 Platte River","Street2":"Suite A","Street3":"REI","PostalCode":802026161,"Longitude":-105.01,"Latitude":39.76},{"index":17,"StoreNumber":"10496-100761","Name":"Park Ave & Globeville- Denver","PhoneNumber":"303-433-1217","Street1":"3470 Park Ave West","Street2":null,"Street3":null,"PostalCode":802165105,"Longitude":-104.99,"Latitude":39.77},{"index":18,"StoreNumber":"6657-73731","Name":"Writer Square","PhoneNumber":"720-932-6574","Street1":"1512 Larimer Street","Street2":null,"Street3":"Writer's Square","PostalCode":802021610,"Longitude":-105,"Latitude":39.75},{"index":19,"StoreNumber":"11474-105326","Name":"Hampden & Locust, Denver","PhoneNumber":"303 691-3437","Street1":"6395 E. Hampden Ave.","Street2":null,"Street3":null,"PostalCode":802227602,"Longitude":-104.92,"Latitude":39.65},{"index":20,"StoreNumber":"74254-65021","Name":"Safeway-Denver #1038","PhoneNumber":"303-691-2960","Street1":"1653 S Colorado Blvd","Street2":null,"Street3":null,"PostalCode":802201055,"Longitude":-104.94,"Latitude":39.69},{"index":21,"StoreNumber":"8971-94801","Name":"Alameda & Downing","PhoneNumber":"303-733-0246","Street1":"1209 East Alameda Ave","Street2":null,"Street3":null,"PostalCode":802092420,"Longitude":-104.97,"Latitude":39.71},{"index":22,"StoreNumber":"16946-106016","Name":"King Soopers-Denver #93","PhoneNumber":"303-333-3223","Street1":"2810 Quebec St.","Street2":null,"Street3":null,"PostalCode":802072900,"Longitude":-104.9,"Latitude":39.76},{"index":23,"StoreNumber":"253-136","Name":"2nd Ave and Filmore - Cherry Creek","PhoneNumber":"(303) 388-7565","Street1":"200 Fillmore Street","Street2":null,"Street3":null,"PostalCode":802065028,"Longitude":-104.95,"Latitude":39.72},{"index":24,"StoreNumber":"6381-25487","Name":"18th & California","PhoneNumber":"303-382-1236","Street1":"633 17th St","Street2":"110","Street3":null,"PostalCode":802023616,"Longitude":-104.99,"Latitude":39.75},{"index":25,"StoreNumber":"24712-197869","Name":"King Soopers Store #115 Denver","PhoneNumber":"303-678-2433","Street1":"1950 Chestnut Pl","Street2":null,"Street3":null,"PostalCode":80202,"Longitude":-105,"Latitude":39.76},{"index":26,"StoreNumber":"2946-87321","Name":"Colfax & Milwaukee-Denver","PhoneNumber":"303-316-2822","Street1":"2975 E Colfax Ave","Street2":null,"Street3":null,"PostalCode":802061604,"Longitude":-104.95,"Latitude":39.74},{"index":27,"StoreNumber":"6214-38","Name":"University & Asbury","PhoneNumber":"(303) 733-9377","Street1":"2000 S. University","Street2":"#B","Street3":null,"PostalCode":802104367,"Longitude":-104.96,"Latitude":39.68},{"index":28,"StoreNumber":"270-138","Name":"8th & Colorado","PhoneNumber":"(303) 329-0040","Street1":"801 Colorado Blvd.","Street2":null,"Street3":null,"PostalCode":802064037,"Longitude":-104.94,"Latitude":39.73},{"index":29,"StoreNumber":"10019-98985","Name":"Speer & Federal - Denver","PhoneNumber":"303-477-5234","Street1":"2990 North Speer Blvd","Street2":"Unit 1","Street3":null,"PostalCode":802114252,"Longitude":-105.02,"Latitude":39.76},{"index":30,"StoreNumber":"5434-872","Name":"University Hills","PhoneNumber":"3037536722","Street1":"2700 S. Colorado Blvd.","Street2":null,"Street3":"University Hills Mall","PostalCode":802103506,"Longitude":-104.94,"Latitude":39.67},{"index":31,"StoreNumber":"10770-100758","Name":"44th Ave & Federal Blvd-Denver","PhoneNumber":"303-477-1457","Street1":"2990 W 44th Ave","Street2":null,"Street3":null,"PostalCode":802111408,"Longitude":-105.02,"Latitude":39.78},{"index":32,"StoreNumber":"6822-82361","Name":"Colfax & Kalamath","PhoneNumber":"303-260-7030","Street1":"1050 W Colfax Ave","Street2":null,"Street3":"Plaza Zocalo","PostalCode":802042072,"Longitude":-105,"Latitude":39.74},{"index":33,"StoreNumber":"24838-238076","Name":"17th and Lawrence","PhoneNumber":"303-242-4741","Street1":"1125 17th Street","Street2":null,"Street3":null,"PostalCode":80202,"Longitude":-104.99,"Latitude":39.75},{"index":34,"StoreNumber":"48194-254256","Name":"Breckenridge at Sloans","PhoneNumber":"7209047014","Street1":"Colfax Ave. and Raleigh Street","Street2":null,"Street3":null,"PostalCode":80204,"Longitude":-105.04,"Latitude":39.74},{"index":35,"StoreNumber":"10625-100287","Name":"45th & Peoria-Denver","PhoneNumber":"303-371-0089","Street1":"4505 Peoria Street","Street2":null,"Street3":null,"PostalCode":802394848,"Longitude":-104.85,"Latitude":39.78},{"index":36,"StoreNumber":"27847-248910","Name":"Chambers and 40th","PhoneNumber":"7205820998","Street1":"4005 Chambers Road","Street2":null,"Street3":null,"PostalCode":80239,"Longitude":-104.81,"Latitude":39.77},{"index":37,"StoreNumber":"75960-108269","Name":"DEN B RJ Term South Finger Lower","PhoneNumber":"303-342-6655","Street1":"HMSHost 8500 Pena Blvd","Street2":null,"Street3":null,"PostalCode":80249,"Longitude":-104.67,"Latitude":39.86},{"index":38,"StoreNumber":"11473-103805","Name":"40th & Colorado-Denver","PhoneNumber":"303 320-7610","Street1":"4030 Colorado Blvd","Street2":"101","Street3":null,"PostalCode":802164303,"Longitude":-104.94,"Latitude":39.77},{"index":39,"StoreNumber":"75004-70380","Name":"King Soopers-Denver #19","PhoneNumber":"303-937-4400","Street1":"3100 S Sheridan","Street2":null,"Street3":null,"PostalCode":802275541,"Longitude":-105.05,"Latitude":39.66},{"index":40,"StoreNumber":"75324-95897","Name":"Atrium @ 1700 Broadway","PhoneNumber":"3038320771","Street1":"1700 Broadway","Street2":null,"Street3":null,"PostalCode":802901700,"Longitude":-104.99,"Latitude":39.74},{"index":41,"StoreNumber":"73787-111045","Name":"King Soopers - Denver #1","PhoneNumber":null,"Street1":"1331 N Speer Blvd","Street2":null,"Street3":null,"PostalCode":80204,"Longitude":-105,"Latitude":39.74},{"index":42,"StoreNumber":"248-135","Name":"Larimer Square","PhoneNumber":"(303) 446-8534","Street1":"1406 Larimer St","Street2":null,"Street3":null,"PostalCode":802021705,"Longitude":-105,"Latitude":39.75},{"index":43,"StoreNumber":"74125-25289","Name":"Safeway-Denver #1614","PhoneNumber":"303-777-1231","Street1":"560 Corona St.","Street2":null,"Street3":null,"PostalCode":802183436,"Longitude":-104.97,"Latitude":39.73},{"index":44,"StoreNumber":"8115-95975","Name":"Colfax & Krameria","PhoneNumber":"303-355-2658","Street1":"6160 E. Colfax Ave","Street2":"Unit 1","Street3":null,"PostalCode":802201569,"Longitude":-104.92,"Latitude":39.74},{"index":45,"StoreNumber":"73566-81441","Name":"Safeway - Denver #3836","PhoneNumber":"303-340-4455","Street1":"200 Quebec St","Street2":"#400","Street3":null,"PostalCode":802307145,"Longitude":-104.9,"Latitude":39.72},{"index":46,"StoreNumber":"17562-165406","Name":"14th & Speer, Denver","PhoneNumber":"3035340351","Street1":"1410 Speer Blvd","Street2":null,"Street3":null,"PostalCode":80204,"Longitude":-105,"Latitude":39.74},{"index":47,"StoreNumber":"5803-15714","Name":"16th St. & Blake","PhoneNumber":"7209043650","Street1":"1490 16th Street","Street2":null,"Street3":"16 Market Square","PostalCode":802021320,"Longitude":-105,"Latitude":39.75},{"index":48,"StoreNumber":"8916-95345","Name":"Colorado & Arizona","PhoneNumber":"303-758-0625","Street1":"1233 S. Colorado Blvd","Street2":null,"Street3":null,"PostalCode":802462902,"Longitude":-104.94,"Latitude":39.69},{"index":49,"StoreNumber":"5392-873","Name":"16th Street & Curtis","PhoneNumber":"(303) 454-9852","Street1":"934 Sixteenth Street","Street2":null,"Street3":"16th & Curtis","PostalCode":802022902,"Longitude":-104.99,"Latitude":39.75},{"index":50,"StoreNumber":"5640-2442","Name":"16th & Tremont","PhoneNumber":"303/575-6753","Street1":"303 16th Street","Street2":"Ste. 100","Street3":null,"PostalCode":802025657,"Longitude":-104.99,"Latitude":39.74},{"index":51,"StoreNumber":"22162-218186","Name":"1900 Sixteenth St.","PhoneNumber":"3036233414","Street1":"1900 Sixteenth St.","Street2":"100","Street3":null,"PostalCode":80202,"Longitude":-105,"Latitude":39.75},{"index":52,"StoreNumber":"74178-32999","Name":"Safeway-Denver #100","PhoneNumber":"303-355-5486","Street1":"3110  E  1st Ave","Street2":null,"Street3":null,"PostalCode":802065614,"Longitude":-104.95,"Latitude":39.72},{"index":53,"StoreNumber":"73165-21582","Name":"Safeway - Denver #885","PhoneNumber":"303-744-8531","Street1":"323 S Broadway","Street2":null,"Street3":null,"PostalCode":802091511,"Longitude":-104.99,"Latitude":39.71},{"index":54,"StoreNumber":"6697-73722","Name":"18th & Champa","PhoneNumber":"303-297-1798","Street1":"999 18th Street","Street2":"113","Street3":"Denver Place Mall","PostalCode":802022499,"Longitude":-104.99,"Latitude":39.75},{"index":55,"StoreNumber":"11034-104012","Name":"Hampden & Dayton - Denver","PhoneNumber":"303 369-5081","Street1":"9925 East Hampden Ave","Street2":null,"Street3":null,"PostalCode":802314903,"Longitude":-104.87,"Latitude":39.65},{"index":56,"StoreNumber":"5285-803","Name":"6th & Grant (Lattas)","PhoneNumber":"(303) 765-5789","Street1":"300 E. 6th Ave.","Street2":"#5","Street3":null,"PostalCode":802036630,"Longitude":-104.98,"Latitude":39.73},{"index":57,"StoreNumber":"8863-95109","Name":"102nd & Grant-Thornton","PhoneNumber":"303-255-3069","Street1":"10005 Grant St, Unit F","Street2":null,"Street3":null,"PostalCode":802292033,"Longitude":-104.98,"Latitude":39.88},{"index":58,"StoreNumber":"10436-98953","Name":"I-70 & I-270- Stapleton","PhoneNumber":"303-371-1503","Street1":"7990 E. 49th Avenue","Street2":null,"Street3":null,"PostalCode":802071709,"Longitude":-104.9,"Latitude":39.79},{"index":59,"StoreNumber":"6540-62379","Name":"Monaco & Evans","PhoneNumber":"303-504-4600","Street1":"2223 S. Monaco Parkway","Street2":"G110","Street3":"Villa Monaco","PostalCode":802225893,"Longitude":-104.91,"Latitude":39.68},{"index":60,"StoreNumber":"10861-103209","Name":"Cherry Creek Mall - West.","PhoneNumber":"303-320-7560","Street1":"3000 1st Ave.","Street2":null,"Street3":null,"PostalCode":802065638,"Longitude":-104.95,"Latitude":39.72},{"index":61,"StoreNumber":"76470-97550","Name":"SuperTarget  Denver/Stapltn ST-2052","PhoneNumber":"303-209-8720","Street1":"7930 E 49th Ave","Street2":null,"Street3":null,"PostalCode":802071709,"Longitude":-104.9,"Latitude":39.78},{"index":62,"StoreNumber":"29429-253366","Name":"Alameda and Federal","PhoneNumber":"7204989109","Street1":"2790 W. Alameda Ave.","Street2":null,"Street3":null,"PostalCode":80219,"Longitude":-105.02,"Latitude":39.71},{"index":63,"StoreNumber":"75959-108270","Name":"DEN B RJ Term South Finger Upper","PhoneNumber":"303-342-6655","Street1":"HMSHost 8500 Pena Blvd.","Street2":null,"Street3":null,"PostalCode":80249,"Longitude":-104.67,"Latitude":39.86},{"index":64,"StoreNumber":"6225-283","Name":"Hampden & Happy Canyon","PhoneNumber":"(303) 639-3102","Street1":"5074 E. Hampden Avenue","Street2":null,"Street3":null,"PostalCode":802227329,"Longitude":-104.93,"Latitude":39.65},{"index":65,"StoreNumber":"10673-100271","Name":"16th & California- Denver","PhoneNumber":"303-534-1096","Street1":"700 16th St.","Street2":"Suite A","Street3":null,"PostalCode":802023229,"Longitude":-104.99,"Latitude":39.75},{"index":66,"StoreNumber":"74282-65040","Name":"Safeway - Denver #2246","PhoneNumber":"303-861-8169","Street1":"757 E 20th Ave","Street2":null,"Street3":null,"PostalCode":802053278,"Longitude":-104.98,"Latitude":39.75},{"index":67,"StoreNumber":"10351-99161","Name":"Alameda & Broadway- Denver","PhoneNumber":"303-722-1002","Street1":"381 S Broadway","Street2":null,"Street3":null,"PostalCode":802091522,"Longitude":-104.99,"Latitude":39.71},{"index":68,"StoreNumber":"10261-100356","Name":"Franklin & Evans-Denver","PhoneNumber":"303-722-2901","Street1":"1605 E Evans Ave","Street2":null,"Street3":null,"PostalCode":802104608,"Longitude":-104.97,"Latitude":39.68},{"index":69,"StoreNumber":"74705-96758","Name":"Safeway-Denver #2714","PhoneNumber":"303-242-3523","Street1":"6220 E 14th St","Street2":null,"Street3":null,"PostalCode":802202747,"Longitude":-104.92,"Latitude":39.74},{"index":70,"StoreNumber":"75506-33861","Name":"Denver Marriott CC - Lobby","PhoneNumber":"303-297-1300","Street1":"1701 California Street","Street2":null,"Street3":"Denver Marriott City Center","PostalCode":802023402,"Longitude":-104.99,"Latitude":39.75},{"index":71,"StoreNumber":"6324-9153","Name":"6th & Lincoln","PhoneNumber":"303-831-4996","Street1":"575 Lincoln St","Street2":"Suite A","Street3":"The Shops at 6th and Broadway","PostalCode":802033469,"Longitude":-104.99,"Latitude":39.73},{"index":72,"StoreNumber":"12848-95170","Name":"King Soopers-Denver #124","PhoneNumber":"303-320-3100","Street1":"4600 Leetsdale Dr","Street2":null,"Street3":null,"PostalCode":802461311,"Longitude":-104.93,"Latitude":39.71},{"index":73,"StoreNumber":"78024-107376","Name":"The Curtis Hotel Lobby","PhoneNumber":"720-889-4730","Street1":"1405 Curtis St","Street2":null,"Street3":null,"PostalCode":802022349,"Longitude":-105,"Latitude":39.75},{"index":74,"StoreNumber":"2817-86203","Name":"Stapleton TC-29th & Quebec","PhoneNumber":"303-322-2477","Street1":"7304 E. 29th Ave","Street2":null,"Street3":null,"PostalCode":802382699,"Longitude":-104.9,"Latitude":39.76},{"index":75,"StoreNumber":"5425-1281","Name":"18th & Franklin - Denver, CO","PhoneNumber":"(303) 320-5518","Street1":"1600 E 18th Ave","Street2":null,"Street3":null,"PostalCode":802181202,"Longitude":-104.97,"Latitude":39.74},{"index":76,"StoreNumber":"9193-151286","Name":"King Soopers-Denver, CO #26","PhoneNumber":"303-758-1210","Street1":"6470 E Hampden Ave","Street2":null,"Street3":null,"PostalCode":802227605,"Longitude":-104.91,"Latitude":39.65},{"index":77,"StoreNumber":"237-230005","Name":"3rd and Columbine-Cherry Creek","PhoneNumber":"3033319910","Street1":"250 Columbine","Street2":"160","Street3":null,"PostalCode":80206,"Longitude":-104.96,"Latitude":39.72}]},"total_rows":78,"truncation_type":null},"text/plain":"     StoreNumber                            Name     PhoneNumber  \\\n0   27708-240564  Auraria Tivoli - Student Union      3035563700   \n1    75828-94910              Grand Hyatt Denver    303-295-1234   \n2    74105-23807          Safeway - Denver #1463    303-722-6117   \n3   22871-102491          Safeway - Denver #1248    303-477-5091   \n4   11105-100844        Highlands Garden Village    303-455-3382   \n..           ...                             ...             ...   \n73  78024-107376          The Curtis Hotel Lobby    720-889-4730   \n74    2817-86203      Stapleton TC-29th & Quebec    303-322-2477   \n75     5425-1281    18th & Franklin - Denver, CO  (303) 320-5518   \n76   9193-151286     King Soopers-Denver, CO #26    303-758-1210   \n77    237-230005  3rd and Columbine-Cherry Creek      3033319910   \n\n                Street1 Street2 Street3  PostalCode  Longitude  Latitude  \n0   900 Auraria Parkway     NaN     NaN       80204    -105.01     39.75  \n1        1740 Welton St     NaN     NaN   802023940    -104.99     39.75  \n2        2150 S Downing     NaN     NaN   802104528    -104.97     39.68  \n3     2660 Federal Blvd     NaN     NaN       80211    -105.02     39.76  \n4       4500 W 38th Ave     NaN     NaN   802122001    -105.05     39.77  \n..                  ...     ...     ...         ...        ...       ...  \n73       1405 Curtis St     NaN     NaN   802022349    -105.00     39.75  \n74     7304 E. 29th Ave     NaN     NaN   802382699    -104.90     39.76  \n75      1600 E 18th Ave     NaN     NaN   802181202    -104.97     39.74  \n76   6470 E Hampden Ave     NaN     NaN   802227605    -104.91     39.65  \n77        250 Columbine     160     NaN       80206    -104.96     39.72  \n\n[78 rows x 9 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>StoreNumber</th>\n      <th>Name</th>\n      <th>PhoneNumber</th>\n      <th>Street1</th>\n      <th>Street2</th>\n      <th>Street3</th>\n      <th>PostalCode</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>27708-240564</td>\n      <td>Auraria Tivoli - Student Union</td>\n      <td>3035563700</td>\n      <td>900 Auraria Parkway</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>80204</td>\n      <td>-105.01</td>\n      <td>39.75</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>75828-94910</td>\n      <td>Grand Hyatt Denver</td>\n      <td>303-295-1234</td>\n      <td>1740 Welton St</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802023940</td>\n      <td>-104.99</td>\n      <td>39.75</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>74105-23807</td>\n      <td>Safeway - Denver #1463</td>\n      <td>303-722-6117</td>\n      <td>2150 S Downing</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802104528</td>\n      <td>-104.97</td>\n      <td>39.68</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>22871-102491</td>\n      <td>Safeway - Denver #1248</td>\n      <td>303-477-5091</td>\n      <td>2660 Federal Blvd</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>80211</td>\n      <td>-105.02</td>\n      <td>39.76</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>11105-100844</td>\n      <td>Highlands Garden Village</td>\n      <td>303-455-3382</td>\n      <td>4500 W 38th Ave</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802122001</td>\n      <td>-105.05</td>\n      <td>39.77</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>78024-107376</td>\n      <td>The Curtis Hotel Lobby</td>\n      <td>720-889-4730</td>\n      <td>1405 Curtis St</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802022349</td>\n      <td>-105.00</td>\n      <td>39.75</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>2817-86203</td>\n      <td>Stapleton TC-29th &amp; Quebec</td>\n      <td>303-322-2477</td>\n      <td>7304 E. 29th Ave</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802382699</td>\n      <td>-104.90</td>\n      <td>39.76</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>5425-1281</td>\n      <td>18th &amp; Franklin - Denver, CO</td>\n      <td>(303) 320-5518</td>\n      <td>1600 E 18th Ave</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802181202</td>\n      <td>-104.97</td>\n      <td>39.74</td>\n    </tr>\n    <tr>\n      <th>76</th>\n      <td>9193-151286</td>\n      <td>King Soopers-Denver, CO #26</td>\n      <td>303-758-1210</td>\n      <td>6470 E Hampden Ave</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802227605</td>\n      <td>-104.91</td>\n      <td>39.65</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>237-230005</td>\n      <td>3rd and Columbine-Cherry Creek</td>\n      <td>3033319910</td>\n      <td>250 Columbine</td>\n      <td>160</td>\n      <td>NaN</td>\n      <td>80206</td>\n      <td>-104.96</td>\n      <td>39.72</td>\n    </tr>\n  </tbody>\n</table>\n<p>78 rows × 9 columns</p>\n</div>"},"metadata":{}}]},{"source":"neighborhoods = gpd.read_file('./data/neighborhoods.shp')\nneighborhoods","metadata":{},"id":"d23db358-bc4c-4558-b42c-4dedf9f15978","cell_type":"code","execution_count":130,"outputs":[{"output_type":"execute_result","execution_count":130,"data":{"text/plain":"    NBHD_ID             NBHD_NAME  \\\n0         2               Auraria   \n1        21        Cory - Merrill   \n2         7               Belcaro   \n3        70       Washington Park   \n4        71  Washington Park West   \n..      ...                   ...   \n73       77              Whittier   \n74       18                  Cole   \n75       76              Westwood   \n76       62             Sunnyside   \n77       23                   DIA   \n\n                                             geometry  \n0   POLYGON ((-105.00042 39.74552, -105.00041 39.7...  \n1   POLYGON ((-104.94070 39.69540, -104.94070 39.6...  \n2   POLYGON ((-104.94070 39.71156, -104.94069 39.7...  \n3   POLYGON ((-104.95931 39.71566, -104.95931 39.7...  \n4   POLYGON ((-104.97342 39.68982, -104.97356 39.6...  \n..                                                ...  \n73  POLYGON ((-104.95977 39.75072, -104.96038 39.7...  \n74  POLYGON ((-104.95975 39.76199, -104.96037 39.7...  \n75  POLYGON ((-105.03970 39.71125, -105.03849 39.7...  \n76  POLYGON ((-104.99818 39.78256, -104.99820 39.7...  \n77  POLYGON ((-104.76269 39.79833, -104.76290 39.7...  \n\n[78 rows x 3 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>NBHD_ID</th>\n      <th>NBHD_NAME</th>\n      <th>geometry</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2</td>\n      <td>Auraria</td>\n      <td>POLYGON ((-105.00042 39.74552, -105.00041 39.7...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>21</td>\n      <td>Cory - Merrill</td>\n      <td>POLYGON ((-104.94070 39.69540, -104.94070 39.6...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7</td>\n      <td>Belcaro</td>\n      <td>POLYGON ((-104.94070 39.71156, -104.94069 39.7...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>70</td>\n      <td>Washington Park</td>\n      <td>POLYGON ((-104.95931 39.71566, -104.95931 39.7...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>71</td>\n      <td>Washington Park West</td>\n      <td>POLYGON ((-104.97342 39.68982, -104.97356 39.6...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>77</td>\n      <td>Whittier</td>\n      <td>POLYGON ((-104.95977 39.75072, -104.96038 39.7...</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>18</td>\n      <td>Cole</td>\n      <td>POLYGON ((-104.95975 39.76199, -104.96037 39.7...</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>76</td>\n      <td>Westwood</td>\n      <td>POLYGON ((-105.03970 39.71125, -105.03849 39.7...</td>\n    </tr>\n    <tr>\n      <th>76</th>\n      <td>62</td>\n      <td>Sunnyside</td>\n      <td>POLYGON ((-104.99818 39.78256, -104.99820 39.7...</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>23</td>\n      <td>DIA</td>\n      <td>POLYGON ((-104.76269 39.79833, -104.76290 39.7...</td>\n    </tr>\n  </tbody>\n</table>\n<p>78 rows × 3 columns</p>\n</div>"},"metadata":{}}]},{"source":"census = pd.read_csv('./data/census.csv')\ncensus","metadata":{},"id":"02f642fa-94e3-4339-8061-d8fbc4e54e66","cell_type":"code","execution_count":131,"outputs":[{"output_type":"execute_result","execution_count":131,"data":{"application/com.datacamp.data-table.v1+json":{"table":{"schema":{"fields":[{"name":"index","type":"integer"},{"name":"NBHD_ID","type":"integer"},{"name":"NBHD_NAME","type":"string"},{"name":"POPULATION_2010","type":"number"},{"name":"AGE_LESS_18","type":"number"},{"name":"AGE_18_TO_34","type":"number"},{"name":"AGE_35_TO_65","type":"number"},{"name":"AGE_65_PLUS","type":"number"},{"name":"NUM_HOUSEHOLDS","type":"number"},{"name":"FAMILIES","type":"number"},{"name":"NUM_HHLD_100K+","type":"number"}],"primaryKey":["index"],"pandas_version":"1.4.0"},"data":[{"index":0,"NBHD_ID":1,"NBHD_NAME":"Athmar 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2406.0   \n1         2        Auraria            705.0          7.0         635.0   \n2         3          Baker           4879.0        745.0        1762.0   \n3         4         Barnum           6111.0       2015.0        1647.0   \n4         5    Barnum West           5376.0       1579.0        1380.0   \n..      ...            ...              ...          ...           ...   \n73       74    West Colfax           9740.0       2756.0        2935.0   \n74       75  West Highland           8540.0       1304.0        2555.0   \n75       76       Westwood          15486.0       5678.0        4037.0   \n76       77       Whittier           4831.0       1119.0        1586.0   \n77       78        Windsor          12589.0       1819.0        3302.0   \n\n    AGE_35_TO_65  AGE_65_PLUS  NUM_HOUSEHOLDS  FAMILIES  NUM_HHLD_100K+  \n0         3112.0        731.0          2781.0    1964.0           425.0  \n1           57.0          6.0            60.0      15.0             NaN  \n2         2037.0        335.0          2447.0     833.0             NaN  \n3         2089.0        360.0          1782.0    1289.0           120.0  \n4         1824.0        593.0          1704.0    1207.0             NaN  \n..           ...          ...             ...       ...             ...  \n73        3111.0        938.0          3703.0    1971.0           431.0  \n74        3756.0        925.0          4256.0    1853.0          1725.0  \n75        4712.0       1059.0          4340.0    3306.0           326.0  \n76        1788.0        338.0          1993.0    1008.0             NaN  \n77        4085.0       3383.0          7204.0    2590.0           723.0  \n\n[78 rows x 10 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>NBHD_ID</th>\n      <th>NBHD_NAME</th>\n      <th>POPULATION_2010</th>\n      <th>AGE_LESS_18</th>\n      <th>AGE_18_TO_34</th>\n      <th>AGE_35_TO_65</th>\n      <th>AGE_65_PLUS</th>\n      <th>NUM_HOUSEHOLDS</th>\n      <th>FAMILIES</th>\n      <th>NUM_HHLD_100K+</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Athmar Park</td>\n      <td>8898.0</td>\n      <td>2649.0</td>\n      <td>2406.0</td>\n      <td>3112.0</td>\n      <td>731.0</td>\n      <td>2781.0</td>\n      <td>1964.0</td>\n      <td>425.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Auraria</td>\n      <td>705.0</td>\n      <td>7.0</td>\n      <td>635.0</td>\n      <td>57.0</td>\n      <td>6.0</td>\n      <td>60.0</td>\n      <td>15.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Baker</td>\n      <td>4879.0</td>\n      <td>745.0</td>\n      <td>1762.0</td>\n      <td>2037.0</td>\n      <td>335.0</td>\n      <td>2447.0</td>\n      <td>833.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>Barnum</td>\n      <td>6111.0</td>\n      <td>2015.0</td>\n      <td>1647.0</td>\n      <td>2089.0</td>\n      <td>360.0</td>\n      <td>1782.0</td>\n      <td>1289.0</td>\n      <td>120.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>Barnum West</td>\n      <td>5376.0</td>\n      <td>1579.0</td>\n      <td>1380.0</td>\n      <td>1824.0</td>\n      <td>593.0</td>\n      <td>1704.0</td>\n      <td>1207.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>74</td>\n      <td>West Colfax</td>\n      <td>9740.0</td>\n      <td>2756.0</td>\n      <td>2935.0</td>\n      <td>3111.0</td>\n      <td>938.0</td>\n      <td>3703.0</td>\n      <td>1971.0</td>\n      <td>431.0</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>75</td>\n      <td>West Highland</td>\n      <td>8540.0</td>\n      <td>1304.0</td>\n      <td>2555.0</td>\n      <td>3756.0</td>\n      <td>925.0</td>\n      <td>4256.0</td>\n      <td>1853.0</td>\n      <td>1725.0</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>76</td>\n      <td>Westwood</td>\n      <td>15486.0</td>\n      <td>5678.0</td>\n      <td>4037.0</td>\n      <td>4712.0</td>\n      <td>1059.0</td>\n      <td>4340.0</td>\n      <td>3306.0</td>\n      <td>326.0</td>\n    </tr>\n    <tr>\n      <th>76</th>\n      <td>77</td>\n      <td>Whittier</td>\n      <td>4831.0</td>\n      <td>1119.0</td>\n      <td>1586.0</td>\n      <td>1788.0</td>\n      <td>338.0</td>\n      <td>1993.0</td>\n      <td>1008.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>78</td>\n      <td>Windsor</td>\n      <td>12589.0</td>\n      <td>1819.0</td>\n      <td>3302.0</td>\n      <td>4085.0</td>\n      <td>3383.0</td>\n      <td>7204.0</td>\n      <td>2590.0</td>\n      <td>723.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>78 rows × 10 columns</p>\n</div>"},"metadata":{}}]},{"source":"## 💪 Challenge\nProvide your client a list of neighborhoods in Denver where they should consider expanding. Include:\n* A visualization of Denver's neighborhoods and the Starbucks store locations.\n* Find the neighborhoods with the highest proportion of people in the target demographic.\n* Select the top three neighborhoods where your client should focus their search.","metadata":{},"id":"54e0ae0c-ff96-443e-9ff3-718f1ab846e5","cell_type":"markdown"},{"source":"### A visualization of Denver's neighborhoods and the Starbucks store locations.","metadata":{},"id":"b2ead333-6040-44ea-a948-5a055789dc60","cell_type":"markdown"},{"source":"denver.head()","metadata":{},"id":"a32d88e2-f3ec-4460-ad8f-68f7f8afc26f","cell_type":"code","execution_count":132,"outputs":[{"output_type":"execute_result","execution_count":132,"data":{"application/com.datacamp.data-table.v1+json":{"table":{"schema":{"fields":[{"name":"index","type":"integer"},{"name":"StoreNumber","type":"string"},{"name":"Name","type":"string"},{"name":"PhoneNumber","type":"string"},{"name":"Street1","type":"string"},{"name":"Street2","type":"string"},{"name":"Street3","type":"string"},{"name":"PostalCode","type":"integer"},{"name":"Longitude","type":"number"},{"name":"Latitude","type":"number"}],"primaryKey":["index"],"pandas_version":"1.4.0"},"data":[{"index":0,"StoreNumber":"27708-240564","Name":"Auraria Tivoli - Student Union","PhoneNumber":"3035563700","Street1":"900 Auraria Parkway","Street2":null,"Street3":null,"PostalCode":80204,"Longitude":-105.01,"Latitude":39.75},{"index":1,"StoreNumber":"75828-94910","Name":"Grand Hyatt Denver","PhoneNumber":"303-295-1234","Street1":"1740 Welton St","Street2":null,"Street3":null,"PostalCode":802023940,"Longitude":-104.99,"Latitude":39.75},{"index":2,"StoreNumber":"74105-23807","Name":"Safeway - Denver #1463","PhoneNumber":"303-722-6117","Street1":"2150 S Downing","Street2":null,"Street3":null,"PostalCode":802104528,"Longitude":-104.97,"Latitude":39.68},{"index":3,"StoreNumber":"22871-102491","Name":"Safeway - Denver #1248","PhoneNumber":"303-477-5091","Street1":"2660 Federal Blvd","Street2":null,"Street3":null,"PostalCode":80211,"Longitude":-105.02,"Latitude":39.76},{"index":4,"StoreNumber":"11105-100844","Name":"Highlands Garden Village","PhoneNumber":"303-455-3382","Street1":"4500 W 38th Ave","Street2":null,"Street3":null,"PostalCode":802122001,"Longitude":-105.05,"Latitude":39.77}]},"total_rows":5,"truncation_type":null},"text/plain":"    StoreNumber                            Name   PhoneNumber  \\\n0  27708-240564  Auraria Tivoli - Student Union    3035563700   \n1   75828-94910              Grand Hyatt Denver  303-295-1234   \n2   74105-23807          Safeway - Denver #1463  303-722-6117   \n3  22871-102491          Safeway - Denver #1248  303-477-5091   \n4  11105-100844        Highlands Garden Village  303-455-3382   \n\n               Street1 Street2 Street3  PostalCode  Longitude  Latitude  \n0  900 Auraria Parkway     NaN     NaN       80204    -105.01     39.75  \n1       1740 Welton St     NaN     NaN   802023940    -104.99     39.75  \n2       2150 S Downing     NaN     NaN   802104528    -104.97     39.68  \n3    2660 Federal Blvd     NaN     NaN       80211    -105.02     39.76  \n4      4500 W 38th Ave     NaN     NaN   802122001    -105.05     39.77  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>StoreNumber</th>\n      <th>Name</th>\n      <th>PhoneNumber</th>\n      <th>Street1</th>\n      <th>Street2</th>\n      <th>Street3</th>\n      <th>PostalCode</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>27708-240564</td>\n      <td>Auraria Tivoli - Student Union</td>\n      <td>3035563700</td>\n      <td>900 Auraria Parkway</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>80204</td>\n      <td>-105.01</td>\n      <td>39.75</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>75828-94910</td>\n      <td>Grand Hyatt Denver</td>\n      <td>303-295-1234</td>\n      <td>1740 Welton St</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802023940</td>\n      <td>-104.99</td>\n      <td>39.75</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>74105-23807</td>\n      <td>Safeway - Denver #1463</td>\n      <td>303-722-6117</td>\n      <td>2150 S Downing</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802104528</td>\n      <td>-104.97</td>\n      <td>39.68</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>22871-102491</td>\n      <td>Safeway - Denver #1248</td>\n      <td>303-477-5091</td>\n      <td>2660 Federal Blvd</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>80211</td>\n      <td>-105.02</td>\n      <td>39.76</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>11105-100844</td>\n      <td>Highlands Garden Village</td>\n      <td>303-455-3382</td>\n      <td>4500 W 38th Ave</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802122001</td>\n      <td>-105.05</td>\n      <td>39.77</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"source":"neighborhoods.head()","metadata":{},"id":"4de65bd0-d549-4692-a5e8-3553f44770bf","cell_type":"code","execution_count":133,"outputs":[{"output_type":"execute_result","execution_count":133,"data":{"text/plain":"   NBHD_ID             NBHD_NAME  \\\n0        2               Auraria   \n1       21        Cory - Merrill   \n2        7               Belcaro   \n3       70       Washington Park   \n4       71  Washington Park West   \n\n                                            geometry  \n0  POLYGON ((-105.00042 39.74552, -105.00041 39.7...  \n1  POLYGON ((-104.94070 39.69540, -104.94070 39.6...  \n2  POLYGON ((-104.94070 39.71156, -104.94069 39.7...  \n3  POLYGON ((-104.95931 39.71566, -104.95931 39.7...  \n4  POLYGON ((-104.97342 39.68982, -104.97356 39.6...  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>NBHD_ID</th>\n      <th>NBHD_NAME</th>\n      <th>geometry</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2</td>\n      <td>Auraria</td>\n      <td>POLYGON ((-105.00042 39.74552, -105.00041 39.7...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>21</td>\n      <td>Cory - Merrill</td>\n      <td>POLYGON ((-104.94070 39.69540, -104.94070 39.6...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7</td>\n      <td>Belcaro</td>\n      <td>POLYGON ((-104.94070 39.71156, -104.94069 39.7...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>70</td>\n      <td>Washington Park</td>\n      <td>POLYGON ((-104.95931 39.71566, -104.95931 39.7...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>71</td>\n      <td>Washington Park West</td>\n      <td>POLYGON ((-104.97342 39.68982, -104.97356 39.6...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"source":"census.head()","metadata":{},"id":"728e26bf-315c-4234-9d1a-751dfc28f137","cell_type":"code","execution_count":134,"outputs":[{"output_type":"execute_result","execution_count":134,"data":{"application/com.datacamp.data-table.v1+json":{"table":{"schema":{"fields":[{"name":"index","type":"integer"},{"name":"NBHD_ID","type":"integer"},{"name":"NBHD_NAME","type":"string"},{"name":"POPULATION_2010","type":"number"},{"name":"AGE_LESS_18","type":"number"},{"name":"AGE_18_TO_34","type":"number"},{"name":"AGE_35_TO_65","type":"number"},{"name":"AGE_65_PLUS","type":"number"},{"name":"NUM_HOUSEHOLDS","type":"number"},{"name":"FAMILIES","type":"number"},{"name":"NUM_HHLD_100K+","type":"number"}],"primaryKey":["index"],"pandas_version":"1.4.0"},"data":[{"index":0,"NBHD_ID":1,"NBHD_NAME":"Athmar Park","POPULATION_2010":8898,"AGE_LESS_18":2649,"AGE_18_TO_34":2406,"AGE_35_TO_65":3112,"AGE_65_PLUS":731,"NUM_HOUSEHOLDS":2781,"FAMILIES":1964,"NUM_HHLD_100K+":425},{"index":1,"NBHD_ID":2,"NBHD_NAME":"Auraria","POPULATION_2010":705,"AGE_LESS_18":7,"AGE_18_TO_34":635,"AGE_35_TO_65":57,"AGE_65_PLUS":6,"NUM_HOUSEHOLDS":60,"FAMILIES":15,"NUM_HHLD_100K+":null},{"index":2,"NBHD_ID":3,"NBHD_NAME":"Baker","POPULATION_2010":4879,"AGE_LESS_18":745,"AGE_18_TO_34":1762,"AGE_35_TO_65":2037,"AGE_65_PLUS":335,"NUM_HOUSEHOLDS":2447,"FAMILIES":833,"NUM_HHLD_100K+":null},{"index":3,"NBHD_ID":4,"NBHD_NAME":"Barnum","POPULATION_2010":6111,"AGE_LESS_18":2015,"AGE_18_TO_34":1647,"AGE_35_TO_65":2089,"AGE_65_PLUS":360,"NUM_HOUSEHOLDS":1782,"FAMILIES":1289,"NUM_HHLD_100K+":120},{"index":4,"NBHD_ID":5,"NBHD_NAME":"Barnum West","POPULATION_2010":5376,"AGE_LESS_18":1579,"AGE_18_TO_34":1380,"AGE_35_TO_65":1824,"AGE_65_PLUS":593,"NUM_HOUSEHOLDS":1704,"FAMILIES":1207,"NUM_HHLD_100K+":null}]},"total_rows":5,"truncation_type":null},"text/plain":"   NBHD_ID    NBHD_NAME  POPULATION_2010  AGE_LESS_18  AGE_18_TO_34  \\\n0        1  Athmar Park           8898.0       2649.0        2406.0   \n1        2      Auraria            705.0          7.0         635.0   \n2        3        Baker           4879.0        745.0        1762.0   \n3        4       Barnum           6111.0       2015.0        1647.0   \n4        5  Barnum West           5376.0       1579.0        1380.0   \n\n   AGE_35_TO_65  AGE_65_PLUS  NUM_HOUSEHOLDS  FAMILIES  NUM_HHLD_100K+  \n0        3112.0        731.0          2781.0    1964.0           425.0  \n1          57.0          6.0            60.0      15.0             NaN  \n2        2037.0        335.0          2447.0     833.0             NaN  \n3        2089.0        360.0          1782.0    1289.0           120.0  \n4        1824.0        593.0          1704.0    1207.0             NaN  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>NBHD_ID</th>\n      <th>NBHD_NAME</th>\n      <th>POPULATION_2010</th>\n      <th>AGE_LESS_18</th>\n      <th>AGE_18_TO_34</th>\n      <th>AGE_35_TO_65</th>\n      <th>AGE_65_PLUS</th>\n      <th>NUM_HOUSEHOLDS</th>\n      <th>FAMILIES</th>\n      <th>NUM_HHLD_100K+</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Athmar Park</td>\n      <td>8898.0</td>\n      <td>2649.0</td>\n      <td>2406.0</td>\n      <td>3112.0</td>\n      <td>731.0</td>\n      <td>2781.0</td>\n      <td>1964.0</td>\n      <td>425.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Auraria</td>\n      <td>705.0</td>\n      <td>7.0</td>\n      <td>635.0</td>\n      <td>57.0</td>\n      <td>6.0</td>\n      <td>60.0</td>\n      <td>15.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Baker</td>\n      <td>4879.0</td>\n      <td>745.0</td>\n      <td>1762.0</td>\n      <td>2037.0</td>\n      <td>335.0</td>\n      <td>2447.0</td>\n      <td>833.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>Barnum</td>\n      <td>6111.0</td>\n      <td>2015.0</td>\n      <td>1647.0</td>\n      <td>2089.0</td>\n      <td>360.0</td>\n      <td>1782.0</td>\n      <td>1289.0</td>\n      <td>120.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>Barnum West</td>\n      <td>5376.0</td>\n      <td>1579.0</td>\n      <td>1380.0</td>\n      <td>1824.0</td>\n      <td>593.0</td>\n      <td>1704.0</td>\n      <td>1207.0</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"source":"from shapely.geometry import Point, MultiPoint, Polygon","metadata":{},"id":"13832330-3556-4a7c-8ceb-d0d2767de2b8","cell_type":"code","execution_count":135,"outputs":[]},{"source":"# use zip\ndenver['geometry'] = [Point(xy) for xy in zip(denver['Longitude'], denver['Latitude'])] \ndenver.head()","metadata":{},"id":"98ca4e42-d25e-41ce-8f39-8c678c91e6a0","cell_type":"code","execution_count":136,"outputs":[{"output_type":"execute_result","execution_count":136,"data":{"text/plain":"    StoreNumber                            Name   PhoneNumber  \\\n0  27708-240564  Auraria Tivoli - Student Union    3035563700   \n1   75828-94910              Grand Hyatt Denver  303-295-1234   \n2   74105-23807          Safeway - Denver #1463  303-722-6117   \n3  22871-102491          Safeway - Denver #1248  303-477-5091   \n4  11105-100844        Highlands Garden Village  303-455-3382   \n\n               Street1 Street2 Street3  PostalCode  Longitude  Latitude  \\\n0  900 Auraria Parkway     NaN     NaN       80204    -105.01     39.75   \n1       1740 Welton St     NaN     NaN   802023940    -104.99     39.75   \n2       2150 S Downing     NaN     NaN   802104528    -104.97     39.68   \n3    2660 Federal Blvd     NaN     NaN       80211    -105.02     39.76   \n4      4500 W 38th Ave     NaN     NaN   802122001    -105.05     39.77   \n\n                geometry  \n0  POINT (-105.01 39.75)  \n1  POINT (-104.99 39.75)  \n2  POINT (-104.97 39.68)  \n3  POINT (-105.02 39.76)  \n4  POINT (-105.05 39.77)  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>StoreNumber</th>\n      <th>Name</th>\n      <th>PhoneNumber</th>\n      <th>Street1</th>\n      <th>Street2</th>\n      <th>Street3</th>\n      <th>PostalCode</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n      <th>geometry</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>27708-240564</td>\n      <td>Auraria Tivoli - Student Union</td>\n      <td>3035563700</td>\n      <td>900 Auraria Parkway</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>80204</td>\n      <td>-105.01</td>\n      <td>39.75</td>\n      <td>POINT (-105.01 39.75)</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>75828-94910</td>\n      <td>Grand Hyatt Denver</td>\n      <td>303-295-1234</td>\n      <td>1740 Welton St</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802023940</td>\n      <td>-104.99</td>\n      <td>39.75</td>\n      <td>POINT (-104.99 39.75)</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>74105-23807</td>\n      <td>Safeway - Denver #1463</td>\n      <td>303-722-6117</td>\n      <td>2150 S Downing</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802104528</td>\n      <td>-104.97</td>\n      <td>39.68</td>\n      <td>POINT (-104.97 39.68)</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>22871-102491</td>\n      <td>Safeway - Denver #1248</td>\n      <td>303-477-5091</td>\n      <td>2660 Federal Blvd</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>80211</td>\n      <td>-105.02</td>\n      <td>39.76</td>\n      <td>POINT (-105.02 39.76)</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>11105-100844</td>\n      <td>Highlands Garden Village</td>\n      <td>303-455-3382</td>\n      <td>4500 W 38th Ave</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802122001</td>\n      <td>-105.05</td>\n      <td>39.77</td>\n      <td>POINT (-105.05 39.77)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"source":"# use gpd\ndenver1= gpd.GeoDataFrame(denver, geometry=gpd.points_from_xy(denver['Longitude'], denver['Latitude']))\ndenver1.head()","metadata":{},"id":"3f76d617-0b0c-4ba9-a964-d1c9bbdd63d2","cell_type":"code","execution_count":137,"outputs":[{"output_type":"execute_result","execution_count":137,"data":{"text/plain":"    StoreNumber                            Name   PhoneNumber  \\\n0  27708-240564  Auraria Tivoli - Student Union    3035563700   \n1   75828-94910              Grand Hyatt Denver  303-295-1234   \n2   74105-23807          Safeway - Denver #1463  303-722-6117   \n3  22871-102491          Safeway - Denver #1248  303-477-5091   \n4  11105-100844        Highlands Garden Village  303-455-3382   \n\n               Street1 Street2 Street3  PostalCode  Longitude  Latitude  \\\n0  900 Auraria Parkway     NaN     NaN       80204    -105.01     39.75   \n1       1740 Welton St     NaN     NaN   802023940    -104.99     39.75   \n2       2150 S Downing     NaN     NaN   802104528    -104.97     39.68   \n3    2660 Federal Blvd     NaN     NaN       80211    -105.02     39.76   \n4      4500 W 38th Ave     NaN     NaN   802122001    -105.05     39.77   \n\n                      geometry  \n0  POINT (-105.01000 39.75000)  \n1  POINT (-104.99000 39.75000)  \n2  POINT (-104.97000 39.68000)  \n3  POINT (-105.02000 39.76000)  \n4  POINT (-105.05000 39.77000)  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>StoreNumber</th>\n      <th>Name</th>\n      <th>PhoneNumber</th>\n      <th>Street1</th>\n      <th>Street2</th>\n      <th>Street3</th>\n      <th>PostalCode</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n      <th>geometry</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>27708-240564</td>\n      <td>Auraria Tivoli - Student Union</td>\n      <td>3035563700</td>\n      <td>900 Auraria Parkway</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>80204</td>\n      <td>-105.01</td>\n      <td>39.75</td>\n      <td>POINT (-105.01000 39.75000)</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>75828-94910</td>\n      <td>Grand Hyatt Denver</td>\n      <td>303-295-1234</td>\n      <td>1740 Welton St</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802023940</td>\n      <td>-104.99</td>\n      <td>39.75</td>\n      <td>POINT (-104.99000 39.75000)</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>74105-23807</td>\n      <td>Safeway - Denver #1463</td>\n      <td>303-722-6117</td>\n      <td>2150 S Downing</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802104528</td>\n      <td>-104.97</td>\n      <td>39.68</td>\n      <td>POINT (-104.97000 39.68000)</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>22871-102491</td>\n      <td>Safeway - Denver #1248</td>\n      <td>303-477-5091</td>\n      <td>2660 Federal Blvd</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>80211</td>\n      <td>-105.02</td>\n      <td>39.76</td>\n      <td>POINT (-105.02000 39.76000)</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>11105-100844</td>\n      <td>Highlands Garden Village</td>\n      <td>303-455-3382</td>\n      <td>4500 W 38th Ave</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802122001</td>\n      <td>-105.05</td>\n      <td>39.77</td>\n      <td>POINT (-105.05000 39.77000)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"source":"neighborhoods_geo=neighborhoods['geometry']\nneighborhoods_geo.head()","metadata":{},"id":"5e9f778a-933d-45e4-8360-34ff11c6c86d","cell_type":"code","execution_count":138,"outputs":[{"output_type":"execute_result","execution_count":138,"data":{"text/plain":"0    POLYGON ((-105.00042 39.74552, -105.00041 39.7...\n1    POLYGON ((-104.94070 39.69540, -104.94070 39.6...\n2    POLYGON ((-104.94070 39.71156, -104.94069 39.7...\n3    POLYGON ((-104.95931 39.71566, -104.95931 39.7...\n4    POLYGON ((-104.97342 39.68982, -104.97356 39.6...\nName: geometry, dtype: geometry"},"metadata":{}}]},{"source":"crs={'init':'epsg:4326'}","metadata":{},"id":"26a229a8-6b59-493d-ab59-5a46bbbffd15","cell_type":"code","execution_count":139,"outputs":[]},{"source":"cities_geo=gpd.GeoDataFrame(denver,crs=crs, geometry=denver['geometry'])\ncities_geo","metadata":{},"id":"a6e4700b-adbb-445c-a9c5-16030a8ec2c0","cell_type":"code","execution_count":140,"outputs":[{"output_type":"execute_result","execution_count":140,"data":{"text/plain":"     StoreNumber                            Name     PhoneNumber  \\\n0   27708-240564  Auraria Tivoli - Student Union      3035563700   \n1    75828-94910              Grand Hyatt Denver    303-295-1234   \n2    74105-23807          Safeway - Denver #1463    303-722-6117   \n3   22871-102491          Safeway - Denver #1248    303-477-5091   \n4   11105-100844        Highlands Garden Village    303-455-3382   \n..           ...                             ...             ...   \n73  78024-107376          The Curtis Hotel Lobby    720-889-4730   \n74    2817-86203      Stapleton TC-29th & Quebec    303-322-2477   \n75     5425-1281    18th & Franklin - Denver, CO  (303) 320-5518   \n76   9193-151286     King Soopers-Denver, CO #26    303-758-1210   \n77    237-230005  3rd and Columbine-Cherry Creek      3033319910   \n\n                Street1 Street2 Street3  PostalCode  Longitude  Latitude  \\\n0   900 Auraria Parkway     NaN     NaN       80204    -105.01     39.75   \n1        1740 Welton St     NaN     NaN   802023940    -104.99     39.75   \n2        2150 S Downing     NaN     NaN   802104528    -104.97     39.68   \n3     2660 Federal Blvd     NaN     NaN       80211    -105.02     39.76   \n4       4500 W 38th Ave     NaN     NaN   802122001    -105.05     39.77   \n..                  ...     ...     ...         ...        ...       ...   \n73       1405 Curtis St     NaN     NaN   802022349    -105.00     39.75   \n74     7304 E. 29th Ave     NaN     NaN   802382699    -104.90     39.76   \n75      1600 E 18th Ave     NaN     NaN   802181202    -104.97     39.74   \n76   6470 E Hampden Ave     NaN     NaN   802227605    -104.91     39.65   \n77        250 Columbine     160     NaN       80206    -104.96     39.72   \n\n                       geometry  \n0   POINT (-105.01000 39.75000)  \n1   POINT (-104.99000 39.75000)  \n2   POINT (-104.97000 39.68000)  \n3   POINT (-105.02000 39.76000)  \n4   POINT (-105.05000 39.77000)  \n..                          ...  \n73  POINT (-105.00000 39.75000)  \n74  POINT (-104.90000 39.76000)  \n75  POINT (-104.97000 39.74000)  \n76  POINT (-104.91000 39.65000)  \n77  POINT (-104.96000 39.72000)  \n\n[78 rows x 10 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>StoreNumber</th>\n      <th>Name</th>\n      <th>PhoneNumber</th>\n      <th>Street1</th>\n      <th>Street2</th>\n      <th>Street3</th>\n      <th>PostalCode</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n      <th>geometry</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>27708-240564</td>\n      <td>Auraria Tivoli - Student Union</td>\n      <td>3035563700</td>\n      <td>900 Auraria Parkway</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>80204</td>\n      <td>-105.01</td>\n      <td>39.75</td>\n      <td>POINT (-105.01000 39.75000)</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>75828-94910</td>\n      <td>Grand Hyatt Denver</td>\n      <td>303-295-1234</td>\n      <td>1740 Welton St</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802023940</td>\n      <td>-104.99</td>\n      <td>39.75</td>\n      <td>POINT (-104.99000 39.75000)</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>74105-23807</td>\n      <td>Safeway - Denver #1463</td>\n      <td>303-722-6117</td>\n      <td>2150 S Downing</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802104528</td>\n      <td>-104.97</td>\n      <td>39.68</td>\n      <td>POINT (-104.97000 39.68000)</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>22871-102491</td>\n      <td>Safeway - Denver #1248</td>\n      <td>303-477-5091</td>\n      <td>2660 Federal Blvd</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>80211</td>\n      <td>-105.02</td>\n      <td>39.76</td>\n      <td>POINT (-105.02000 39.76000)</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>11105-100844</td>\n      <td>Highlands Garden Village</td>\n      <td>303-455-3382</td>\n      <td>4500 W 38th Ave</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802122001</td>\n      <td>-105.05</td>\n      <td>39.77</td>\n      <td>POINT (-105.05000 39.77000)</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>78024-107376</td>\n      <td>The Curtis Hotel Lobby</td>\n      <td>720-889-4730</td>\n      <td>1405 Curtis St</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802022349</td>\n      <td>-105.00</td>\n      <td>39.75</td>\n      <td>POINT (-105.00000 39.75000)</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>2817-86203</td>\n      <td>Stapleton TC-29th &amp; Quebec</td>\n      <td>303-322-2477</td>\n      <td>7304 E. 29th Ave</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802382699</td>\n      <td>-104.90</td>\n      <td>39.76</td>\n      <td>POINT (-104.90000 39.76000)</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>5425-1281</td>\n      <td>18th &amp; Franklin - Denver, CO</td>\n      <td>(303) 320-5518</td>\n      <td>1600 E 18th Ave</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802181202</td>\n      <td>-104.97</td>\n      <td>39.74</td>\n      <td>POINT (-104.97000 39.74000)</td>\n    </tr>\n    <tr>\n      <th>76</th>\n      <td>9193-151286</td>\n      <td>King Soopers-Denver, CO #26</td>\n      <td>303-758-1210</td>\n      <td>6470 E Hampden Ave</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>802227605</td>\n      <td>-104.91</td>\n      <td>39.65</td>\n      <td>POINT (-104.91000 39.65000)</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>237-230005</td>\n      <td>3rd and Columbine-Cherry Creek</td>\n      <td>3033319910</td>\n      <td>250 Columbine</td>\n      <td>160</td>\n      <td>NaN</td>\n      <td>80206</td>\n      <td>-104.96</td>\n      <td>39.72</td>\n      <td>POINT (-104.96000 39.72000)</td>\n    </tr>\n  </tbody>\n</table>\n<p>78 rows × 10 columns</p>\n</div>"},"metadata":{}}]},{"source":"cities_geo.plot()","metadata":{},"id":"c948ddba-b383-44cf-b43d-a1bc7beb0436","cell_type":"code","execution_count":141,"outputs":[{"output_type":"execute_result","execution_count":141,"data":{"text/plain":"<AxesSubplot:>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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qLK2Xn16xGlERqKinI6O7haAToagAUSgTQcqtHBDiSqqv6pwnOKK04IJWRp/aUoLrwSA9sWpEyDCbDpQodlr3vEJGZJ0tLpes9e8o00H/NceAoD2RtAAIojbY2nhhhKd7571pmULN5TI7ekUd7UDCAEEDSCCvFV2otkvGWezJFVU1+utshPB6xSATo2gAUSQylr/IaM17QCgrQgaQATp0yOwafkDbQcAbUXQACLIiIxEpbji5O8mVofO3H0yIiMxmN0C0IkRNIAIEuV0aMGELElqFjaa/r1gQhbzaQAIGoIGEGHGX5qigh8PU7LL9/RIsitOBT8exjwaAIKKCbuACDT+0hSNzUpmZlAAHY6gAUSoKKdD2YMu6uhuAOjkOHUCAACMIWgAAABjCBoAAMAYrtEIElMluykFDn8i/diI9PEBkYKgEQSmSnZTChz+RPqxEenjAyKJw7KsTlHGsaamRi6XS9XV1UpISAja+zaV7D53Izf93dXaeQ1MrRfhL9KPjUgfHxAuAv1e5RoNg0yV7KYUOPyJ9GMj0scHRCKChkGmSnZTChz+RPqxEenjAyIRQcMgUyW7KQUOfyL92Ij08QGRiKBhkKmS3ZQChz+RfmxE+viASETQMMhUyW5KgcOfSD82In18QCQiaBhkqmQ3pcDhT6QfG5E+PiASETQMM1Wym1Lg8CfSj41IHx8QaZhHI0iYGRTBFunHRqSPDwh1gX6vEjQAAIBtTNgFAAA6HEEDAAAYQ1G1NuAc8VcifVtE+vgQHBxH6IwIGq1E9civRPq2iPTxITg4jtBZcTFoK1A98iuRvi0ifXwIDo4jRCIuBjWE6pFfifRtEenjQ3BwHKGzI2jYRPXIr0T6toj08SE4OI7Q2RE0bKJ65FcifVtE+vgQHBxH6OwIGjZRPfIrkb4tIn18CA6OI3R2BA2bqB75lUjfFpE+PgQHxxE6O4KGTVSP/Eqkb4tIHx+Cg+MInR1BoxWoHvmVSN8WkT4+BAfHEToz5tFoA2b5+0qkb4tIHx+Cg+MIkYTqreegeisAAO2HCbsAAECHI2gAAABjCBoAAMAYqreGucbTHj1bXK4jJ05pQGI33ZKdrpho//nRzsVoXLgW3uweGwBgAheDhrElG0u06o0ynV2LyemQZo3MUP41Wc3a2ylTTUnr8Gb32AAAu7gYNMIt2Viila/7fpFIkseSVr5epiUbS3yWN5WpPre409Hqes1e8442HahoVVuEHrvHBgCYRNAIQ42nPVr1RlmLbVa9UabG0x5J9spUU9I6vNk9NgDANIJGGHq2uLzZX6vn8lhn2kn2ylRT0jq82T02AMA0LgYNQ0dOnLLVzkSZakpahya7xwYAmEbQCEMDErvZameiTDUlrUOT3WMDAEzj1EkYuiU7XRe6y9TpONNOslemmpLW4c3usQEAphE0wlBMtFOzRma02GbWyAzvnAl2ylRT0jq82T02AMA0Pm3CVP41Wbrt6oxmf706HdJtVzefK8FOmWpKWoc3u8cGAJjEhF1hjplB4Q8zgwIwiTLx54jUoAEAQEdgZlAAANDhCBoAAMAY5tHoZOxcd1F96kvNfOotfVpdr1RXnP44Y4Rc3bqct+0/axo0+fEdOlH3pRK7d9H6O76jpITYNvcBABDebF2jUVBQoIKCApWXl0uShgwZovnz5ys3N1eSVFpaqnvuuUc7duxQQ0ODxo8frxUrVqhv375+15menq4jR440W37HHXfod7/7nSTpe9/7nl577TWf52+77TY98cQTgXadazRkryLrd5cX6cjxL5qtY8BFXfXanFE+y4Y+UKia+tPN2ibERevdB8a1ug8AgNBl5BqNfv36aenSpdqzZ4/efvttjRo1SpMmTdLBgwdVV1ennJwcORwOFRUVaefOnWpsbNSECRPk8fgv4LR7925VVFR4H1u2bJEkTZ061afdrFmzfNo99NBDdrre6dmpyOovZEjSkeNf6LvLi7z/9hcyJKmm/rSGPlDYqj4AACKDrVMnEyZM8Pn3okWLVFBQoDfffFP/+Mc/VF5err1793qTzdNPP61evXqpqKhIY8aMOe86k5KSfP69dOlSDRo0SN/97nd9lnfr1k3Jycl2uov/70IVWR06U5F1bFayPq8/7TdkNDly/AtVn/pSjac9fkNGk5r60/pnTYMS42MC7gOnUQAgcrT6YlC32621a9eqrq5O2dnZamhokMPhUGzsV+fl4+Li5HQ6tWPHjoDW2djYqDVr1mjmzJlyOHy/bP70pz+pd+/euvTSS5Wfn69Tp1ouCtXQ0KCamhqfR2dlpyLrzKfeCmidM596S5MfD2y/Tn58B1VhAaCTsn0x6P79+5Wdna36+nrFx8dr/fr1ysrKUlJSkrp37665c+dq8eLFsixL8+bNk9vtVkVFYD+Jv/TSS6qqqtKMGTN8lv/oRz/SgAEDlJqaqnfffVdz587V4cOH9Ze//MXvupYsWaKFCxfaHV5EslO99dMWwsDZPq2uV/WpLwNqe6LuSyMVZAEAoc/2LxqZmZnat2+fdu3apdmzZ2v69OkqKSlRUlKS1q1bpw0bNig+Pl4ul0tVVVUaNmyYnM7A3ubJJ59Ubm6uUlNTfZb/5Cc/0bhx43TZZZfp5ptv1jPPPKP169ertLTU77ry8/NVXV3tfXz88cd2hxox7FRvTXUF1jbVFafE7ue/A+Vcid27GKkgCwAIfbZ/0YiJidHgwYMlScOHD9fu3bv16KOPauXKlcrJyVFpaak+++wzRUdHq2fPnkpOTtbAgQMvuN4jR45o69atLf5K0eRb3/qWJOmDDz7QoEGDztsmNjbW5zROZ9ZUkfVodf15r5Fw6EwdkxEZifrjjBH6l/+z+YLr/OOMEWo87dG/Lt56wbbr7/iOEuNjAu4DACBytHnCLo/Ho4aGBp9lvXv3Vs+ePVVUVKTKykpNnDjxgutZvXq1+vTpo2uvvfaCbfft2ydJSknhdshA2KnI6urWRQMu6tri+gZc1FWubl2UlBCrhLiWs2pCXLSSEmKpCgsAnZStoJGfn6/XX39d5eXl2r9/v/Lz87V9+3bdfPPNks6EhTfffFOlpaVas2aNpk6dqry8PGVmZnrXMXr0aD322GM+6/V4PFq9erWmT5+u6GjfL67S0lI9+OCD2rNnj8rLy/Xyyy9r2rRpuvrqqzV06NDWjrvTsVOR9bU5o/yGjXPn0Xj3gXF+w8a582hQFRYAOh9bp04qKys1bdo0VVRUyOVyaejQoSosLNTYsWMlSYcPH1Z+fr5OnDih9PR03XfffcrLy/NZR9OplbNt3bpVH330kWbOnNnsPWNiYrR161b913/9l+rq6pSWlqYpU6boV7/6ld2xdnrjL03R2KzkgGblfG3OqIBnBn33gXEBzwxqpw8AgPBH9VYAAGAb1VsBAECHI2gAAABjCBoAAMAYysQHSTiWRv+i0a3FG0tUfvyU0i/qpnuvyVLXmKjztv28/rTy/u9efXTyC/Xv1VWP/PAKxV/g1tdAmdp2obJPGk979GxxuY6cOKUBid10S3a6YqL5GwBAZOBi0CAIx9Los57ZrS0llc2Wj83qo1XT/tVn2cTH3tC7nzSvJTO0X4JevnNkm/phatuFyj5ZsrFEq94ok+es/4VOhzRrZIbyr8kKWj8AwC4uBg0R4Vga3V/IkKQtJZWa9cxu77/9hQxJeveTGk187I1W98PUtguVfbJkY4lWvu4bMiTJY0krXy/Tko0lQekHAJhE0DDoQuXZpTOl0d3nftN0oC8a3X5DRpMtJZX6otGtz+tP+w0ZTd79pEafX6CU/PmY2nahsk8aT3u06o2yFtuseqNMjac9RvsBAKYRNAwKx9LoiwP8K3rxxhLl/d+9AbUNtN3ZTG27UNknzxaXN/sl41we60w7AAhnXAxqUDiWRi8/firgdpW1DRduKOmjk1/Y7oepbRcq++TIicC2c6DtACBU8YuGQeFYGj39om4Bt+vfq+Xia00CbXc2U9suVPbJgMTAtnOg7QAgVBE0DGoqz+7vhkmHztzpEEql0e8N8E6He6/J0iM/vCKgtoG2O5upbRcq++SW7HRd6E5ap+NMOwAIZwQNg8KxNHrXmCiNzerTYpuxWX3UNSZK8XHRGtqv5VuFh/ZLaNV8Gqa2Xajsk5hop2aNzGixzayRGcynASDs8SlmWDiWRl817V/9ho1z59F4+c6RfsNGW+fRMLXtQmWf5F+Tpduuzmj2y4bTId12NfNoAIgMTNgVJKEyC6UdzAwaHMwMCiAcBfq9StAAAAC2MTMoAADocAQNAABgDEEDAAAYw8ygaBd2Lqy0exFmKFwsGSoXjgJAuCFooM3slFy3W579fGXUF208FNQy6qFSUh4AwhGnTtAmdkqu2y3PHgpl1EOlpDwAhCuCBlrNTsl1u+XZQ6GMeqiUlAeAcEbQQKvZKblutzx7KJRRD5WS8gAQzrhGA61mouR6U9tQKKMeKiXlASCcETTQaiZKrje1DYUy6qFSUh4AwhmnTtBqdkqu2y3PHgpl1EOlpDwAhDOCBlrNTsl1u+XZQ6GMeqiUlAeAcEbQQJvYKblutzx7KJRRD5WS8gAQrqjeinbBzKAA0LlQJv4cBA0AANoPZeIBAECHI2gAAABjmEcDEcXOtRRfNLq1eGOJyo+fUvpF3XTvNVnqGhMV5B6bEwrXtgAA12ggYtipsjrrmd3aUlLZbB1js/po1bR/Nd5X085X9dbpUFCr3gKIbFyjgU7FTpVVfyFDkraUVGrWM7uN9tW0UKh6CwBNCBoIe3aqrH7R6PYbMppsKanUF43udu9nMIRC1VsAOBtBA2HPTpXVxQH+NR9ou1ATClVvAeBsBA2EPTtVVsuPB1btNdB2oSYUqt4CwNkIGgh7dqqspl8UWLXXQNuFmlCoegsAZyNoIOzZqbJ6b4B3XATaLtSEQtVbADgbQQNhz06V1a4xURqb1afF9Y3N6hO282mEQtVbADgbnzaICHaqrK6a9q9+w0YkzKMRClVvAaAJE3YhojAz6FeYGRSASVRvPQdBAwCA9sPMoAAAoMMRNAAAgDEEDQAAYAxl4sOcnYsfTTJ54WGojDFQdvsbbuMDADsIGmHMTll0k85XknzRxkPtUpI8VMYYKLv9DbfxAYBd3HUSpprKop+785r+Dj537ghTmkqS+9OWeRtCZYyBstvfcBsfAJyNu04imJ2y6CaZLEkeKmMMlN3+htv4AKC1CBphyE5ZdJNMliQPlTEGym5/w218ANBaBI0wZKcsukkmS5KHyhgDZbe/4TY+AGgtgkYYslMW3SSTJclDZYyBstvfcBsfALQWQSMM2SmLbpLJkuShMsZA2e1vuI0PAFqLoBGG7JRFN8lkSfJQGWOg7PY33MYHAK1F0AhTdsqim2SyJHmojDFQdvsbbuMDgNZgHo0wFyqzSjIz6FeYGRRAZ0CZ+HNEatAAAKAjMGEXAADocAQNAABgDEEDAAAYYytoFBQUaOjQoUpISFBCQoKys7P1yiuveJ8vLS3V5MmTlZSUpISEBN1www06duxYi+tMT0+Xw+Fo9vjpT3/qbVNfX6+f/vSnuuiiixQfH68pU6ZccL0AAKDj2Qoa/fr109KlS7Vnzx69/fbbGjVqlCZNmqSDBw+qrq5OOTk5cjgcKioq0s6dO9XY2KgJEybI4/FfVGv37t2qqKjwPrZs2SJJmjp1qrdNXl6eNmzYoHXr1um1117Tp59+qu9///utHDIAAAiWNt91kpiYqOXLlystLU25ubk6efKk9+rT6upq9erVS5s3b9aYMWMCWt/dd9+tv/71r3r//fflcDhUXV2tpKQkPffcc/rBD34gSfrf//1fXXLJJSouLtaVV14Z0Hq56wQAgPZj/K4Tt9uttWvXqq6uTtnZ2WpoaJDD4VBsbKy3TVxcnJxOp3bs2BHQOhsbG7VmzRrNnDlTDseZeQT27NmjL7/80ieoXHzxxerfv7+Ki4v9rquhoUE1NTU+DwAAEFy2g8b+/fsVHx+v2NhY3X777Vq/fr2ysrJ05ZVXqnv37po7d65OnTqluro63XPPPXK73aqoqAho3S+99JKqqqo0Y8YM77KjR48qJiZGPXv29Gnbt29fHT161O+6lixZIpfL5X2kpaXZHSoAAGgj20EjMzNT+/bt065duzR79mxNnz5dJSUlSkpK0rp167RhwwbFx8fL5XKpqqpKw4YNk9MZ2Ns8+eSTys3NVWpqqu2BnCs/P1/V1dXex8cff9zmdQIAAHui7b4gJiZGgwcPliQNHz5cu3fv1qOPPqqVK1cqJydHpaWl+uyzzxQdHa2ePXsqOTlZAwcOvOB6jxw5oq1bt+ovf/mLz/Lk5GQ1NjaqqqrK51eNY8eOKTk52e/6YmNjfU7jAACA4GvzPBoej0cNDQ0+y3r37q2ePXuqqKhIlZWVmjhx4gXXs3r1avXp00fXXnutz/Lhw4erS5cu2rZtm3fZ4cOH9dFHHyk7O7ut3QcAAAbZ+kUjPz9fubm56t+/v2pra/Xcc89p+/btKiwslHQmLFxyySVKSkpScXGx7rrrLuXl5SkzM9O7jtGjR2vy5Mm68847vcs8Ho9Wr16t6dOnKzrat0sul0u33nqrfv7znysxMVEJCQn6j//4D2VnZwd8xwkAAOgYtoJGZWWlpk2bpoqKCrlcLg0dOlSFhYUaO3aspDO/NOTn5+vEiRNKT0/Xfffdp7y8PJ91NJ1aOdvWrVv10UcfaebMmed930ceeUROp1NTpkxRQ0ODxo0bp8cff9xO1wEAQAegeisAALCN6q0AAKDDETQAAIAxBA0AAGCM7Xk0gPNxeyy9VXZClbX16tMjTiMyEhXldHR0t9pN42mPni0u15ETpzQgsZtuyU5XTDQ5HQAuhKCBNtt0oEILN5SoorreuyzFFacFE7I0/tKUDuxZ+1iysUSr3iiT56zLphdtPKRZIzOUf01Wx3UMAMIAf5KhTTYdqNDsNe/4hAxJOlpdr9lr3tGmA4HVuQlVSzaWaOXrviFDkjyWtPL1Mi3ZWNIxHQOAMEHQQKu5PZYWbijR+e6Pblq2cEOJ3Od+S4eJxtMerXqjrMU2q94oU+NpT5B6BADhh6CBVnur7ESzXzLOZkmqqK7XW2UngtepdvRscXmzXzLO5bHOtAMAnB9BA61WWes/ZLSmXag5cuJUu7YDgM6IoIFW69Mjrl3bhZoBid3atR0AdEYEDbTaiIxEpbji5O8mVofO3H0yIiMxmN1qN7dkp+tCd+g6HWfaAQDOj6CBVotyOrRgwpnbO8/9Pm7694IJWWE7n0ZMtFOzRma02GbWyAzm0wCAFvAJiTYZf2mKCn48TMku39Mjya44Ffx4WNjPo5F/TZZuuzqj2S8bTod029XMowEAF0L1VrQLZgYFgM4l0O9VZgZFu4hyOpQ96KKO7oYxMdFO3TpyYEd3AwDCDn+SAQAAYwgaAADAGIIGAAAwhms0EPLsXIgZ6RelRvr4AEQeggZCmp0S7ZFerj7SxwcgMnHqBCHLTon2SC9XH+njAxC5CBoISXZKtEd6ufpIHx+AyEbQQEiyU6I90svVR/r4AEQ2rtFASLJTor13j9iA2oZrufpA+x2u4wMQ2fhFAyHJTon2SC9XH+njAxDZCBoISXZKtEd6ufpIHx+AyEbQQEiyU6I90svVR/r4AEQ2ggZClp0S7ZFerj7SxwcgclEmHiGPmUG/EunjAxA+Av1eJWgAAADbAv1e5dQJAAAwhqABAACMYcIuIAB2rhMxiWs0AIQbggZwAXYqyJpE9VYA4YhTJ0AL7FSQNYnqrQDCFUED8MNOBVmTqN4KIJwRNAA/7FSQNYnqrQDCGUED8MNOBVmTqN4KIJwRNAA/7FSQNYnqrQDCGUED8MNOBVmTqN4KIJwRNAA/7FSQNYnqrQDCGUEDaIGdCrImUb0VQLiiqBoQAGYGBQBfgX6vMjMoEICYaKduHTmwo7uhKKdD2YMu6uhuAEDAOHUCAACMIWgAAABjCBoAAMAYrtFoAy7MAwCgZQSNVqJkNwAAF8apk1agZDcAAIEhaNhEyW4AAAJH0LCJkt0AAASOoGETJbsBAAgcQcMmSnYDABA4goZNlOwGACBwBA2bKNkNAEDgCBqtQMluAAACw4RdrTT+0hSNzUpmZlAAAFpA0GgDSnYDANAyTp0AAABjCBoAAMAYTp10MlScNc/uNrbT3lRbk0KlHwA6hq2gUVBQoIKCApWXl0uShgwZovnz5ys3N1eSVFpaqnvuuUc7duxQQ0ODxo8frxUrVqhv374trvcf//iH5s6dq1deeUWnTp3S4MGDtXr1an3zm9+UJM2YMUNPP/20z2vGjRunTZs22el+p0fFWfPsbmM77U21NSlU+gGg49g6ddKvXz8tXbpUe/bs0dtvv61Ro0Zp0qRJOnjwoOrq6pSTkyOHw6GioiLt3LlTjY2NmjBhgjwej991njx5Ut/+9rfVpUsXvfLKKyopKdFvfvMb9erVy6fd+PHjVVFR4X08//zzrRtxJ0XFWfPsbmM77U21NSlU+gGgYzksy2pTmdHExEQtX75caWlpys3N1cmTJ5WQkCBJqq6uVq9evbR582aNGTPmvK+fN2+edu7cqTfeeMPve8yYMUNVVVV66aWXWt3PmpoauVwuVVdXe/vXWbg9lr6zrMhvMTiHzswBsmPuKH7SbiW729hOe0lG2prc1xxzQOQL9Hu11ReDut1urV27VnV1dcrOzlZDQ4McDodiY2O9beLi4uR0OrVjxw6/63n55Zf1zW9+U1OnTlWfPn10xRVXaNWqVc3abd++XX369FFmZqZmz56t48ePt9i/hoYG1dTU+Dw6KyrOmmd3G9tpb6qtSaHSDwAdz3bQ2L9/v+Lj4xUbG6vbb79d69evV1ZWlq688kp1795dc+fO1alTp1RXV6d77rlHbrdbFRX+fyL98MMPVVBQoK9//esqLCzU7Nmz9bOf/cznmozx48frmWee0bZt27Rs2TK99tprys3Nldvt9rveJUuWyOVyeR9paWl2hxoxqDhrnt1tbKe9qbYmhUo/AHQ820EjMzNT+/bt065duzR79mxNnz5dJSUlSkpK0rp167RhwwbFx8fL5XKpqqpKw4YNk9Pp/208Ho+GDRumxYsX64orrtBPfvITzZo1S0888YS3zY033qiJEyfqsssu0/XXX6+//vWv2r17t7Zv3+53vfn5+aqurvY+Pv74Y7tDjRhUnDXP7ja2095UW5NCpR8AOp7t21tjYmI0ePBgSdLw4cO1e/duPfroo1q5cqVycnJUWlqqzz77TNHR0erZs6eSk5M1cOBAv+tLSUlRVlaWz7JLLrlEL774ot/XDBw4UL1799YHH3yg0aNHn7dNbGysz2mczqyp4uzR6nqd74KcpvPlVJxtPbvb2G57U21N4ZgD0KTNE3Z5PB41NDT4LOvdu7d69uypoqIiVVZWauLEiX5f/+1vf1uHDx/2Wfbee+9pwIABfl/zySef6Pjx40pJ4fa4QFBx1jy729hOe1NtTQqVfgDoeLaCRn5+vl5//XWVl5dr//79ys/P1/bt23XzzTdLklavXq0333xTpaWlWrNmjaZOnaq8vDxlZmZ61zF69Gg99thj3n/n5eXpzTff1OLFi/XBBx/oueee0+9//3v99Kc/lSR9/vnnmjNnjt58802Vl5dr27ZtmjRpkgYPHqxx48a1xzboFKg4a57dbWynvam2JoVKPwB0LFu3t956663atm2bKioq5HK5NHToUM2dO1djx46VdOZW1aeeekonTpxQenq6br/9duXl5cnh+OqvlvT0dM2YMUMPPPCAd9lf//pX5efn6/3331dGRoZ+/vOfa9asWZKkL774Qtdff7327t2rqqoqpaamKicnRw8++OAFJwI7W2e+vfVszNJoHjODhmY/ALSvQL9X2zyPRrggaAAA0H6Mz6MBAABwIQQNAABgDEEDAAAYQ9AAAADGEDQAAIAxBA0AAGAMQQMAABhD0AAAAMYQNAAAgDG2q7eGq6YJUGtqajq4JwAAhL+m79MLTTDeaYJGbW2tJCktLa2DewIAQOSora2Vy+Xy+3ynqXXi8Xj06aefqkePHj5F3tqqpqZGaWlp+vjjjyOyhgrjC2+ML7xF+vikyB9jJI/PsizV1tYqNTVVTqf/KzE6zS8aTqdT/fr1M7b+hISEiDuIzsb4whvjC2+RPj4p8scYqeNr6ZeMJlwMCgAAjCFoAAAAYwgabRQbG6sFCxYoNja2o7tiBOMLb4wvvEX6+KTIH2Okjy8QneZiUAAAEHz8ogEAAIwhaAAAAGMIGgAAwBiCBgAAMKZTB41FixbpqquuUrdu3dSzZ8/ztvnoo4907bXXqlu3burTp4/mzJmj06dPe5/fvn27HA5Hs8fRo0dbfO93331XI0eOVFxcnNLS0vTQQw/5PP/UU081W2dcXFzEjE+S1q1bp4svvlhxcXG67LLLtHHjxqCP72w7d+5UdHS0Lr/88gu+9wsvvKDLL79c3bp104ABA7R8+XKf51u73cJpjNKZcQ4bNkyxsbEaPHiwnnrqKRuj69jxFRYW6sorr1SPHj2UlJSkKVOmqLy83Gdsbd2HoTy+pjGG4/574IEHzrtvunfv7m0TKp+hpsYntf0zNFg6ddBobGzU1KlTNXv27PM+73a7de2116qxsVF/+9vf9PTTT+upp57S/Pnzm7U9fPiwKioqvI8+ffr4fd+amhrl5ORowIAB2rNnj5YvX64HHnhAv//9733aJSQk+KzzyJEjETO+v/3tb7rpppt06623au/evbr++ut1/fXX68CBAx0yvqqqKk2bNk2jR4++4Pu+8soruvnmm3X77bfrwIEDevzxx/XII4/osccea9bWznYLtzGWlZXp2muv1b/9279p3759uvvuu/Xv//7vKiwsDPnxlZWVadKkSRo1apT27dunwsJCffbZZ/r+97/frG1b9mEojy+c998999zjs08qKiqUlZWlqVOn+rQLpc/Q9h5fe3yGBo0Fa/Xq1ZbL5Wq2fOPGjZbT6bSOHj3qXVZQUGAlJCRYDQ0NlmVZ1quvvmpJsk6ePBnw+z3++ONWr169vOuwLMuaO3eulZmZecE+tUYoju+GG26wrr32Wp/Xfetb37Juu+22gN+nSVvG1+SHP/yh9atf/cpasGCB9S//8i8tvt9NN91k/eAHP/BZ9tvf/tbq16+f5fF4LMtq3XZrSSiO8Ze//KU1ZMiQZu8xbtw4GyM7I9jjW7dunRUdHW253W7vspdfftlyOBxWY2OjZVntuw9DcXzhvP/OtW/fPkuS9frrr1+wT60RiuNrz89Q0zr1LxoXUlxcrMsuu0x9+/b1Lhs3bpxqamp08OBBn7aXX365UlJSNHbsWO3cufOC67366qsVExPjs97Dhw/r5MmT3mWff/65BgwYoLS0NE2aNKnZe7ZVR46vuLhYY8aM8XnduHHjVFxc3NZh+fQjkPGtXr1aH374oRYsWBDQehsaGpr9BNu1a1d98sknzf5isrPdWqMjxxjO+3D48OFyOp1avXq13G63qqur9eyzz2rMmDHq0qWLT1uT+7AjxxfO++9cf/jDH/SNb3xDI0eO9FkeKp+hJsYXjP3XXggaLTh69KjPASTJ+++m87QpKSl64okn9OKLL+rFF19UWlqavve97+mdd95p03ozMzP1xz/+Uf/93/+tNWvWyOPx6KqrrtInn3wSEePz18buNQwtCaQf77//vubNm6c1a9YoOjqwGoPjxo3TX/7yF23btk0ej0fvvfeefvOb30iSKioqJLVuu7VGR47R33vX1NToiy++aNO4mpgaX0ZGhjZv3qx7771XsbGx6tmzpz755BO98MIL3jbB2IcdOb5w3n9nq6+v15/+9CfdeuutPstD5TPU1PiC8RnaXiKueuu8efO0bNmyFtscOnRIF198cbu8X2ZmpjIzM73/vuqqq1RaWqpHHnlEzz77bKvXm52drezsbJ/1XnLJJfrhD3+ov/3tby2+NhzG58/nn38uh8PRYpv2Gp/b7daPfvQjLVy4UN/4xjcCft2sWbNUWlqq6667Tl9++aUSEhJ011136YEHHvCWSm5pu33ta18L2jFqcowt6datW4vPd/T4jh49qlmzZmn69Om66aabVFtbq/nz5+sHP/iBtmzZIofD4XcfTps27YJ/GYfD+FoS6vvvbOvXr1dtba2mT5/uszwUPkNNji+cRFzQ+MUvfqEZM2a02GbgwIEBrSs5OVlvvfWWz7Jjx455n/NnxIgR2rFjR4vrbVpPoOvt0qWLrrjiCp0+fVqHDh1qsd/hMD5/bdLT0/Xiiy+22O/2Gl9tba3efvtt7d27V3feeackyePxyLIsRUdHa/PmzRo1alSz9TocDi1btkyLFy/W0aNHlZSUpG3btl2wb03b7eGHHw7aMWpyjP72YXx8vHbv3h3S4/vd734nl8vlczfUmjVrlJaWpl27dunKK688b39GjBihV199NWj/B02OL5z339n+8Ic/6Lrrrmv21/25OuIz1OT4/O2/lj67O0rEBY2kpCQlJSW1y7qys7O1aNEiVVZWeq8037JlixISEpSVleX3dfv27VNKSkqL673vvvv05Zdfes+XbtmyRZmZmerVq9d5X+N2u7V//35dc8017fZrRUeOLzs7W9u2bdPdd9/tfd2WLVs0cuTIoI2vS5cu2r9/v89rHn/8cRUVFenPf/6zMjIyWlx/VFSUvva1r0mSnn/+eWVnZ7d47DVtt2AeoybHmJ2d3ex2ui1btujb3/52yO/DU6dONftlJioqStKZLwJ/9u3bp/79+0fE+MJ5/zUpKyvTq6++qpdffvmCfemIz1CT4/P3GXr2rzghoyOvRO1oR44csfbu3WstXLjQio+Pt/bu3Wvt3bvXqq2ttSzLsk6fPm1deumlVk5OjrVv3z5r06ZNVlJSkpWfn+9dxyOPPGK99NJL1vvvv2/t37/fuuuuuyyn02lt3brV22bFihXWqFGjvP+uqqqy+vbta91yyy3WgQMHrLVr11rdunWzVq5c6W2zcOFCq7Cw0CotLbX27Nlj3XjjjVZcXJx18ODBiBjfzp07rejoaOvXv/61dejQIWvBggVWly5drP379wd1fOc63xXh547vn//8p1VQUGAdOnTI2rt3r/Wzn/3MiouLs3bt2mVru4X7GD/88EOrW7du1pw5c6xDhw5Zv/vd76yoqChr06ZNIT++bdu2WQ6Hw1q4cKH13nvvWXv27LHGjRtnDRgwwDp16pRlWe2zD0N5fOG8/5r86le/slJTU63Tp083ey5UPkNNja89PkODpVMHjenTp1uSmj1effVVb5vy8nIrNzfX6tq1q9W7d2/rF7/4hfXll196n1+2bJk1aNAgKy4uzkpMTLS+973vWUVFRT7vs2DBAmvAgAE+y/7+979b3/nOd6zY2Fjra1/7mrV06VKf5++++26rf//+VkxMjNW3b1/rmmuusd55552IGZ9lWdYLL7xgfeMb37BiYmKsIUOGWP/zP/8T9PGd63wfAueO75///Kd15ZVXWt27d7e6detmjR492nrzzTd9XhPIdgv3MVrWmVtAL7/8cismJsYaOHCgtXr16rAYn2VZ1vPPP29dccUVVvfu3a2kpCRr4sSJ1qFDh7zPt8c+DOXxWVZ47z+3223169fPuvfee8+7nlD5DDU1Pstq+2dosFAmHgAAGMPtrQAAwBiCBgAAMIagAQAAjCFoAAAAYwgaAADAGIIGAAAwhqABAACMIWgAAABjCBoAAMAYggYAADCGoAEAAIwhaAAAAGP+H1PvWoiXEvzFAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"source":"import matplotlib.pyplot as plt\nfig, ax = plt.subplots()\n\nax.set_aspect('equal')\n\nneighborhoods_geo.plot(ax=ax,facecolor='Blue', edgecolor='k',alpha=1,linewidth=1)\n\ncities_geo.plot(ax=ax, marker='o', color='red', markersize=5)\n\nplt.show()","metadata":{},"id":"f1dc8649-c1e5-49c8-845e-d00fe1ca2f17","cell_type":"code","execution_count":149,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"source":"### Find the neighborhoods with the highest proportion of people in the target demographic.","metadata":{},"id":"c20d47fc-3f04-4aae-8fd1-92f58ca01d00","cell_type":"markdown"},{"source":"neighborhoods.head()","metadata":{},"id":"4ca0da15-3ae0-4228-82c5-4b30e430e7f1","cell_type":"code","execution_count":143,"outputs":[{"output_type":"execute_result","execution_count":143,"data":{"text/plain":"   NBHD_ID             NBHD_NAME  \\\n0        2               Auraria   \n1       21        Cory - Merrill   \n2        7               Belcaro   \n3       70       Washington Park   \n4       71  Washington Park West   \n\n                                            geometry  \n0  POLYGON ((-105.00042 39.74552, -105.00041 39.7...  \n1  POLYGON ((-104.94070 39.69540, -104.94070 39.6...  \n2  POLYGON ((-104.94070 39.71156, -104.94069 39.7...  \n3  POLYGON ((-104.95931 39.71566, -104.95931 39.7...  \n4  POLYGON ((-104.97342 39.68982, -104.97356 39.6...  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>NBHD_ID</th>\n      <th>NBHD_NAME</th>\n      <th>geometry</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2</td>\n      <td>Auraria</td>\n      <td>POLYGON ((-105.00042 39.74552, -105.00041 39.7...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>21</td>\n      <td>Cory - Merrill</td>\n      <td>POLYGON ((-104.94070 39.69540, -104.94070 39.6...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7</td>\n      <td>Belcaro</td>\n      <td>POLYGON ((-104.94070 39.71156, -104.94069 39.7...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>70</td>\n      <td>Washington Park</td>\n      <td>POLYGON ((-104.95931 39.71566, -104.95931 39.7...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>71</td>\n      <td>Washington Park West</td>\n      <td>POLYGON ((-104.97342 39.68982, -104.97356 39.6...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"source":"census.head()","metadata":{},"id":"68093251-85cf-4d24-8ede-7827fd2ffc71","cell_type":"code","execution_count":144,"outputs":[{"output_type":"execute_result","execution_count":144,"data":{"application/com.datacamp.data-table.v1+json":{"table":{"schema":{"fields":[{"name":"index","type":"integer"},{"name":"NBHD_ID","type":"integer"},{"name":"NBHD_NAME","type":"string"},{"name":"POPULATION_2010","type":"number"},{"name":"AGE_LESS_18","type":"number"},{"name":"AGE_18_TO_34","type":"number"},{"name":"AGE_35_TO_65","type":"number"},{"name":"AGE_65_PLUS","type":"number"},{"name":"NUM_HOUSEHOLDS","type":"number"},{"name":"FAMILIES","type":"number"},{"name":"NUM_HHLD_100K+","type":"number"}],"primaryKey":["index"],"pandas_version":"1.4.0"},"data":[{"index":0,"NBHD_ID":1,"NBHD_NAME":"Athmar Park","POPULATION_2010":8898,"AGE_LESS_18":2649,"AGE_18_TO_34":2406,"AGE_35_TO_65":3112,"AGE_65_PLUS":731,"NUM_HOUSEHOLDS":2781,"FAMILIES":1964,"NUM_HHLD_100K+":425},{"index":1,"NBHD_ID":2,"NBHD_NAME":"Auraria","POPULATION_2010":705,"AGE_LESS_18":7,"AGE_18_TO_34":635,"AGE_35_TO_65":57,"AGE_65_PLUS":6,"NUM_HOUSEHOLDS":60,"FAMILIES":15,"NUM_HHLD_100K+":null},{"index":2,"NBHD_ID":3,"NBHD_NAME":"Baker","POPULATION_2010":4879,"AGE_LESS_18":745,"AGE_18_TO_34":1762,"AGE_35_TO_65":2037,"AGE_65_PLUS":335,"NUM_HOUSEHOLDS":2447,"FAMILIES":833,"NUM_HHLD_100K+":null},{"index":3,"NBHD_ID":4,"NBHD_NAME":"Barnum","POPULATION_2010":6111,"AGE_LESS_18":2015,"AGE_18_TO_34":1647,"AGE_35_TO_65":2089,"AGE_65_PLUS":360,"NUM_HOUSEHOLDS":1782,"FAMILIES":1289,"NUM_HHLD_100K+":120},{"index":4,"NBHD_ID":5,"NBHD_NAME":"Barnum West","POPULATION_2010":5376,"AGE_LESS_18":1579,"AGE_18_TO_34":1380,"AGE_35_TO_65":1824,"AGE_65_PLUS":593,"NUM_HOUSEHOLDS":1704,"FAMILIES":1207,"NUM_HHLD_100K+":null}]},"total_rows":5,"truncation_type":null},"text/plain":"   NBHD_ID    NBHD_NAME  POPULATION_2010  AGE_LESS_18  AGE_18_TO_34  \\\n0        1  Athmar Park           8898.0       2649.0        2406.0   \n1        2      Auraria            705.0          7.0         635.0   \n2        3        Baker           4879.0        745.0        1762.0   \n3        4       Barnum           6111.0       2015.0        1647.0   \n4        5  Barnum West           5376.0       1579.0        1380.0   \n\n   AGE_35_TO_65  AGE_65_PLUS  NUM_HOUSEHOLDS  FAMILIES  NUM_HHLD_100K+  \n0        3112.0        731.0          2781.0    1964.0           425.0  \n1          57.0          6.0            60.0      15.0             NaN  \n2        2037.0        335.0          2447.0     833.0             NaN  \n3        2089.0        360.0          1782.0    1289.0           120.0  \n4        1824.0        593.0          1704.0    1207.0             NaN  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>NBHD_ID</th>\n      <th>NBHD_NAME</th>\n      <th>POPULATION_2010</th>\n      <th>AGE_LESS_18</th>\n      <th>AGE_18_TO_34</th>\n      <th>AGE_35_TO_65</th>\n      <th>AGE_65_PLUS</th>\n      <th>NUM_HOUSEHOLDS</th>\n      <th>FAMILIES</th>\n      <th>NUM_HHLD_100K+</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Athmar Park</td>\n      <td>8898.0</td>\n      <td>2649.0</td>\n      <td>2406.0</td>\n      <td>3112.0</td>\n      <td>731.0</td>\n      <td>2781.0</td>\n      <td>1964.0</td>\n      <td>425.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Auraria</td>\n      <td>705.0</td>\n      <td>7.0</td>\n      <td>635.0</td>\n      <td>57.0</td>\n      <td>6.0</td>\n      <td>60.0</td>\n      <td>15.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Baker</td>\n      <td>4879.0</td>\n      <td>745.0</td>\n      <td>1762.0</td>\n      <td>2037.0</td>\n      <td>335.0</td>\n      <td>2447.0</td>\n      <td>833.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>Barnum</td>\n      <td>6111.0</td>\n      <td>2015.0</td>\n      <td>1647.0</td>\n      <td>2089.0</td>\n      <td>360.0</td>\n      <td>1782.0</td>\n      <td>1289.0</td>\n      <td>120.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>Barnum West</td>\n      <td>5376.0</td>\n      <td>1579.0</td>\n      <td>1380.0</td>\n      <td>1824.0</td>\n      <td>593.0</td>\n      <td>1704.0</td>\n      <td>1207.0</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"source":"new_neighborhoods=pd.merge(neighborhoods,census,how='left',on='NBHD_ID')\nnew_neighborhoods['area']=new_neighborhoods['geometry'].area\nnew_neighborhoods['AGE_18_TO_34_area_ratio']=new_neighborhoods['AGE_18_TO_34']/new_neighborhoods['area']\nnew_neighborhoods.sort_values('AGE_18_TO_34_area_ratio',ascending=False,inplace=True)\nnew_neighborhoods.head(3)","metadata":{},"cell_type":"code","id":"ba60d39d-3da4-4f2f-a2e0-8d593f1bf8be","execution_count":145,"outputs":[{"output_type":"execute_result","execution_count":145,"data":{"text/plain":"    NBHD_ID         NBHD_NAME_x  \\\n45        9        Capitol Hill   \n46       47  North Capitol Hill   \n5        59               Speer   \n\n                                             geometry         NBHD_NAME_y  \\\n45  POLYGON ((-104.97341 39.74003, -104.97299 39.7...        Capitol Hill   \n46  POLYGON ((-104.97338 39.74378, -104.97339 39.7...  North Capitol Hill   \n5   POLYGON ((-104.97325 39.71840, -104.97323 39.7...               Speer   \n\n    POPULATION_2010  AGE_LESS_18  AGE_18_TO_34  AGE_35_TO_65  AGE_65_PLUS  \\\n45          14708.0        408.0        8274.0        5155.0        871.0   \n46           5823.0        194.0        3238.0        1968.0        423.0   \n5           10954.0        707.0        5361.0        3970.0        916.0   \n\n    NUM_HOUSEHOLDS  FAMILIES  NUM_HHLD_100K+      area  \\\n45         10856.0    1201.0          1223.0  0.000184   \n46          3851.0     461.0          1010.0  0.000096   \n5           7304.0    1400.0          1682.0  0.000232   \n\n    AGE_18_TO_34_area_ratio  \n45             4.495116e+07  \n46             3.374537e+07  \n5              2.307399e+07  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>NBHD_ID</th>\n      <th>NBHD_NAME_x</th>\n      <th>geometry</th>\n      <th>NBHD_NAME_y</th>\n      <th>POPULATION_2010</th>\n      <th>AGE_LESS_18</th>\n      <th>AGE_18_TO_34</th>\n      <th>AGE_35_TO_65</th>\n      <th>AGE_65_PLUS</th>\n      <th>NUM_HOUSEHOLDS</th>\n      <th>FAMILIES</th>\n      <th>NUM_HHLD_100K+</th>\n      <th>area</th>\n      <th>AGE_18_TO_34_area_ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>45</th>\n      <td>9</td>\n      <td>Capitol Hill</td>\n      <td>POLYGON ((-104.97341 39.74003, -104.97299 39.7...</td>\n      <td>Capitol Hill</td>\n      <td>14708.0</td>\n      <td>408.0</td>\n      <td>8274.0</td>\n      <td>5155.0</td>\n      <td>871.0</td>\n      <td>10856.0</td>\n      <td>1201.0</td>\n      <td>1223.0</td>\n      <td>0.000184</td>\n      <td>4.495116e+07</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>47</td>\n      <td>North Capitol Hill</td>\n      <td>POLYGON ((-104.97338 39.74378, -104.97339 39.7...</td>\n      <td>North Capitol Hill</td>\n      <td>5823.0</td>\n      <td>194.0</td>\n      <td>3238.0</td>\n      <td>1968.0</td>\n      <td>423.0</td>\n      <td>3851.0</td>\n      <td>461.0</td>\n      <td>1010.0</td>\n      <td>0.000096</td>\n      <td>3.374537e+07</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>59</td>\n      <td>Speer</td>\n      <td>POLYGON ((-104.97325 39.71840, -104.97323 39.7...</td>\n      <td>Speer</td>\n      <td>10954.0</td>\n      <td>707.0</td>\n      <td>5361.0</td>\n      <td>3970.0</td>\n      <td>916.0</td>\n      <td>7304.0</td>\n      <td>1400.0</td>\n      <td>1682.0</td>\n      <td>0.000232</td>\n      <td>2.307399e+07</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"source":"### Select the top three neighborhoods where your client should focus their search.","metadata":{},"id":"850d30c9-ef21-4118-b459-3c74e7c61b69","cell_type":"markdown"},{"source":"new_neighborhoods['coords'] = new_neighborhoods['geometry'].apply(lambda x: x.representative_point().coords[:])\nnew_neighborhoods['coords'] = [coords[0] for coords in new_neighborhoods['coords']]\nnew_anno=new_neighborhoods.head(3)\nnew_anno","metadata":{},"cell_type":"code","id":"68c20175-6cce-4c8d-99a4-1a54b9c7476e","execution_count":146,"outputs":[{"output_type":"execute_result","execution_count":146,"data":{"text/plain":"    NBHD_ID         NBHD_NAME_x  \\\n45        9        Capitol Hill   \n46       47  North Capitol Hill   \n5        59               Speer   \n\n                                             geometry         NBHD_NAME_y  \\\n45  POLYGON ((-104.97341 39.74003, -104.97299 39.7...        Capitol Hill   \n46  POLYGON ((-104.97338 39.74378, -104.97339 39.7...  North Capitol Hill   \n5   POLYGON ((-104.97325 39.71840, -104.97323 39.7...               Speer   \n\n    POPULATION_2010  AGE_LESS_18  AGE_18_TO_34  AGE_35_TO_65  AGE_65_PLUS  \\\n45          14708.0        408.0        8274.0        5155.0        871.0   \n46           5823.0        194.0        3238.0        1968.0        423.0   \n5           10954.0        707.0        5361.0        3970.0        916.0   \n\n    NUM_HOUSEHOLDS  FAMILIES  NUM_HHLD_100K+      area  \\\n45         10856.0    1201.0          1223.0  0.000184   \n46          3851.0     461.0          1010.0  0.000096   \n5           7304.0    1400.0          1682.0  0.000232   \n\n    AGE_18_TO_34_area_ratio                                    coords  \n45             4.495116e+07  (-104.98019393047466, 39.73287968286655)  \n46             3.374537e+07  (-104.98053860447689, 39.74400581081494)  \n5              2.307399e+07  (-104.98037487449828, 39.71906601364779)  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>NBHD_ID</th>\n      <th>NBHD_NAME_x</th>\n      <th>geometry</th>\n      <th>NBHD_NAME_y</th>\n      <th>POPULATION_2010</th>\n      <th>AGE_LESS_18</th>\n      <th>AGE_18_TO_34</th>\n      <th>AGE_35_TO_65</th>\n      <th>AGE_65_PLUS</th>\n      <th>NUM_HOUSEHOLDS</th>\n      <th>FAMILIES</th>\n      <th>NUM_HHLD_100K+</th>\n      <th>area</th>\n      <th>AGE_18_TO_34_area_ratio</th>\n      <th>coords</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>45</th>\n      <td>9</td>\n      <td>Capitol Hill</td>\n      <td>POLYGON ((-104.97341 39.74003, -104.97299 39.7...</td>\n      <td>Capitol Hill</td>\n      <td>14708.0</td>\n      <td>408.0</td>\n      <td>8274.0</td>\n      <td>5155.0</td>\n      <td>871.0</td>\n      <td>10856.0</td>\n      <td>1201.0</td>\n      <td>1223.0</td>\n      <td>0.000184</td>\n      <td>4.495116e+07</td>\n      <td>(-104.98019393047466, 39.73287968286655)</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>47</td>\n      <td>North Capitol Hill</td>\n      <td>POLYGON ((-104.97338 39.74378, -104.97339 39.7...</td>\n      <td>North Capitol Hill</td>\n      <td>5823.0</td>\n      <td>194.0</td>\n      <td>3238.0</td>\n      <td>1968.0</td>\n      <td>423.0</td>\n      <td>3851.0</td>\n      <td>461.0</td>\n      <td>1010.0</td>\n      <td>0.000096</td>\n      <td>3.374537e+07</td>\n      <td>(-104.98053860447689, 39.74400581081494)</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>59</td>\n      <td>Speer</td>\n      <td>POLYGON ((-104.97325 39.71840, -104.97323 39.7...</td>\n      <td>Speer</td>\n      <td>10954.0</td>\n      <td>707.0</td>\n      <td>5361.0</td>\n      <td>3970.0</td>\n      <td>916.0</td>\n      <td>7304.0</td>\n      <td>1400.0</td>\n      <td>1682.0</td>\n      <td>0.000232</td>\n      <td>2.307399e+07</td>\n      <td>(-104.98037487449828, 39.71906601364779)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"source":"#use AGE_18_TO_34_area_ratio, target demographic density\nimport matplotlib.pyplot as plt\nplt.figure(dpi=300,figsize=(15,10))\nfig, ax = plt.subplots()\n\nax.set_aspect('equal')\n\nnew_neighborhoods.plot(ax=ax,column='AGE_18_TO_34_area_ratio',facecolor='Grey', edgecolor='k',alpha=1,linewidth=1,cmap=\"jet\",legend=True)\n\nfor idx, row in new_anno.iterrows():\n    ax.annotate(text=row['NBHD_ID'], xy=row['coords'])\n\ncities_geo.plot(ax=ax, marker='o', color='red', markersize=5)\n\n\nax.set_xlabel('Longitude')\nax.set_ylabel('Latitude')\nax.set_title('Neighborhoods')\n\n\nplt.show()","metadata":{},"id":"412f4190-fadc-437c-9ec6-c2ed6e0b1c20","cell_type":"code","execution_count":147,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 4500x3000 with 0 Axes>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAjAAAAGeCAYAAACKDztsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAACyIUlEQVR4nOzdeXwTZf7A8c8k6UUvbspRaLnPyqFooYgCghUR0EVFlEP8uYu4CooKFRBFCoqIgCuii4AisuIKHiuggIAVREBuBKRy23LT0tIryfz+mCRN2rRNmvT+vl+veSWZeeaZJ+kx3zynoqqqihBCCCFEBaIr6wIIIYQQQrhLAhghhBBCVDgSwAghhBCiwpEARgghhBAVjgQwQgghhKhwJIARQgghRIUjAYwQQgghKhwJYIQQQghR4UgAI4QQQogKRwIYIYQQQlQ4EsAIIYQQFcjWrVsZMGAADRo0QFEU1qxZ49b506ZNQ1GUfFtgYGDJFLiESAAjhBBCVCDp6encdNNN/Otf/yrW+RMmTCApKclha9u2LUOGDPFySUuWBDBCCCFEBRIbG8vrr7/O4MGDnR7PyspiwoQJNGzYkMDAQG699VY2b95sOx4UFERYWJhtO3/+PIcPH2b06NGl9A68QwIYIYQQohJ5+umn2b59OytXrmT//v0MGTKEu+++mz/++MNp+n//+9+0bNmSHj16lHJJPSMBjBBCCFFJnD59miVLlrBq1Sp69OhBs2bNmDBhAjExMSxZsiRf+szMTD799NMKV/sCYCjrAgghhBDCOw4cOIDJZKJly5YO+7OysqhVq1a+9KtXr+b69euMGDGitIroNRLACCGEEJVEWloaer2e3bt3o9frHY4FBQXlS//vf/+be++9l3r16pVWEb1GAhghhBCikujUqRMmk4kLFy4U2aflxIkT/Pjjj3z99delVDrvkgBGCCGEqEDS0tI4fvy47fWJEyfYu3cvNWvWpGXLlgwbNozhw4czZ84cOnXqxMWLF9m4cSNRUVH079/fdt5HH31E/fr1iY2NLYu34TFFVVW1rAshhBBCCNds3ryZO++8M9/+ESNGsHTpUnJycnj99df5+OOPOXfuHLVr1+a2227j1VdfpUOHDgCYzWaaNGnC8OHDmTFjRmm/Ba+QAEYIIYQQFY4MoxZCCCFEhSN9YIQQQohKIjMzk+zsbI/z8fX1xd/f3wslKjkSwAghhBCVQGZmJnUCAkjzQl5hYWGcOHGiXAcxEsAIIYQQlUB2djZpwHjAz4N8soC5yclkZ2dLACOEEEKI0hEIeBJ2VJTAoKKUUwghhBAu8LFsxWXyVkFKmIxCEkIIIUSFIzUwQgghRCViwLObe0UJDCpKOYUQQgjhAgOeNSEZvVWQEiYBjBBCCFGJVJUaGOkDI4QQQogKp6IEWkIIIYRwgaejkKQJSQghhBClTpqQhBBCCCHKKQlghKiARo4cSURERLHPDQoKKjLdyZMnURSFt956q1jXcVdERAT33ntvqVzLFdb3v3Tp0rIuihBusY5CKu4mNTBCCJYuXYqiKPj7+3Pu3Ll8x++44w7at29fBiUTQlRWBi9sFYEEMEKUgqysLGbNmuW1/D788EOOHj3qtfyEEKKikQBGiFLQsWNHPvzwQ/766y+v5Ofj44OfnyfrzZYf6enpZV0EISoVT5qPPB3BVJokgBGiFMTFxWEymVyqhVm+fDldunQhICCAmjVr8vDDD3PmzBmHNM76wFy+fJnHHnuMkJAQqlevzogRI9i3b1+B/TjOnTvHoEGDCAoKok6dOkyYMAGTyfkybnPnzqVJkyYEBATQs2dPDh48mC/Npk2b6NGjB4GBgVSvXp2BAwfy+++/O6SZNm0aiqJw+PBhHnnkEWrUqEFMTIxDmoSEBLp27Yq/vz9Nmzbl448/znetP//8kyFDhlCzZk2qVavGbbfdxv/+97986S5cuMDo0aOpV68e/v7+3HTTTSxbtixfumvXrjFy5EhCQ0Ntn921a9fypUtOTmbUqFE0atQIPz8/6tevz8CBAzl58qTTz02IsiABjBDCayIjIxk+fHiRtTAzZsxg+PDhtGjRgrfffptx48axceNGbr/9dqc3VCuz2cyAAQP47LPPGDFiBDNmzCApKYkRI0Y4TW8ymejXrx+1atXirbfeomfPnsyZM4cPPvggX9qPP/6Y+fPnM3bsWCZNmsTBgwfp1asX58+ft6XZsGED/fr148KFC0ybNo3nnnuObdu20b17d6c39yFDhnDjxg3i4+P5v//7P9v+48eP87e//Y277rqLOXPmUKNGDUaOHMmhQ4dsac6fP0+3bt1Yv349Tz31FDNmzCAzM5P77ruP1atX29JlZGRwxx138MknnzBs2DBmz55NaGgoI0eOZN68ebZ0qqoycOBAPvnkEx599FFef/11zp496/Sze+CBB1i9ejWjRo3ivffe45lnnuH69eucPn26wJ+NEKKEqEKIErNkyRIVUHfu3KkmJiaqBoNBfeaZZ2zHe/bsqbZr105VVVU9efKkqtfr1RkzZjjkceDAAdVgMDjsHzFihNqkSRPb6//+978qoL7zzju2fSaTSe3Vq5cKqEuWLHE4F1Bfe+01h+t06tRJ7dKli+31iRMnVEANCAhQz549a9u/Y8cOFVDHjx9v29exY0e1bt266uXLl2379u3bp+p0OnX48OG2fa+88ooKqEOHDs33WTVp0kQF1K1bt9r2XbhwQfXz81Off/55275x48apgPrTTz/Z9l2/fl2NjIxUIyIiVJPJpKqqqr7zzjsqoC5fvtyWLjs7W42OjlaDgoLU1NRUVVVVdc2aNSqgvvnmm7Z0RqNR7dGjh8Nnd/XqVRVQZ8+ena/sQpQHKSkpKqB+Aep3HmxfgAqoKSkpZf2WCiU1MEKUkqZNm/LYY4/xwQcfkJSUlO/4l19+idls5sEHH+TSpUu2LSwsjBYtWvDjjz8WmPe6devw8fFxqM3Q6XSMHTu2wHP+8Y9/OLzu0aMHf/75Z750gwYNomHDhrbXXbt25dZbb+W7774DICkpib179zJy5Ehq1qxpSxcVFcVdd91lS1fYta3atm1Ljx49bK/r1KlDq1atHMr13Xff0bVrV4emp6CgIJ588klOnjzJ4cOHbenCwsIYOnSoLZ2Pjw/PPPMMaWlpbNmyxZbOYDAwZswYWzq9Xs8///lPh7IFBATg6+vL5s2buXr1qtPyC1EeyDBqIYTXTZ48GaPR6LQvzB9//IGqqrRo0YI6deo4bL///jsXLlwoMN9Tp05Rv359qlWr5rC/efPmTtP7+/tTp04dh301atRwemNu0aJFvn0tW7a0NQ2dOnUKgFatWuVL16ZNGy5dupSvo25kZKTTcjVu3DjfvrzlOnXqVIHXsi/PqVOnaNGiBTqdrsh09evXzzc3Tt5r+Pn58cYbb7B27Vrq1avH7bffzptvvklycrLT9yJEWakqw6grSjmFqBSaNm3Ko48+ygcffMDEiRMdjpnNZhRFYe3atej1+nznujL5nKuc5V+aAgICnO4vqFyqqpZkcVw2btw4BgwYwJo1a1i/fj1Tpkxh5syZbNq0iU6dOpV18YSoUiSAEaKUTZ48meXLl/PGG2847G/WrBmqqhIZGUnLli3dyrNJkyb8+OOP3Lhxw6EW5vjx4x6X948//si379ixY7ZRUE2aNAFwOi/NkSNHqF27NoGBgR6Xw6pJkyYFXsu+PE2aNGH//v2YzWaHWhhn6TZu3EhaWppDkFjQPDvNmjXj+eef5/nnn+ePP/6gY8eOzJkzh+XLl3vnDQrhIU9HEskoJCGEU82aNePRRx9l0aJFDs0P999/P3q9nldffTVfjYOqqly+fLnAPPv160dOTg4ffvihbZ/ZbOZf//qXx+Vds2aNwyzCv/76Kzt27CA2NhaA+vXr07FjR5YtW+YwUurgwYN8//333HPPPR6Xwd4999zDr7/+yvbt22370tPT+eCDD4iIiKBt27a2dMnJyfznP/+xpTMajSxYsICgoCB69uxpS2c0Glm4cKEtnclkYsGCBQ7XvXHjBpmZmQ77mjVrRnBwMFlZWV59j0J4QpqQhBAl5uWXX+aTTz7h6NGjtGvXDtBuhq+//jqTJk3i5MmTDBo0iODgYE6cOMHq1at58sknmTBhgtP8Bg0aRNeuXXn++ec5fvw4rVu35uuvv+bKlSsAKIpS7LI2b96cmJgYxowZQ1ZWFu+88w61atXixRdftKWZPXs2sbGxREdHM3r0aDIyMliwYAGhoaFMmzat2Nd2ZuLEiXz22WfExsbyzDPPULNmTZYtW8aJEyf473//a6ttefLJJ1m0aBEjR45k9+7dRERE8MUXX/Dzzz/zzjvvEBwcDMCAAQPo3r07EydO5OTJk7Rt25Yvv/ySlJQUh+seO3aM3r178+CDD9K2bVsMBgOrV6/m/PnzPPzww159j0KIokkAI0QZaN68OY8++mi+SdUmTpxIy5YtmTt3Lq+++ioA4eHh9O3bl/vuu6/A/PR6Pf/73/949tlnWbZsGTqdjsGDB/PKK6/QvXt3/P39i13W4cOHo9PpeOedd7hw4QJdu3bl3XffpX79+rY0ffr0Yd26dbzyyitMnToVHx8fevbsyRtvvFFgh93iqlevHtu2beOll15iwYIFZGZmEhUVxTfffEP//v1t6QICAti8eTMTJ05k2bJlpKam0qpVK5YsWcLIkSNt6XQ6HV9//TXjxo1j+fLlKIrCfffdx5w5cxz6tYSHhzN06FA2btzIJ598gsFgoHXr1nz++ec88MADXn2PQnjCOgrJk/MrAkUtL73jhBBet2bNGgYPHkxCQgLdu3cv6+IIIUpQamoqoaGh/Ax40uU/DegOpKSkEBIS4p3ClQDpAyNEJZGRkeHw2tqPIyQkhM6dO5dRqYQQomRUlJoiIUQR/vnPf5KRkUF0dDRZWVl8+eWXbNu2jfj4+AKHLQshKp+qMgpJAhghKolevXoxZ84cvv32WzIzM2nevDkLFizg6aefLuuiCSFKkacjiSpKYCB9YIQQQohKwNoH5jc87wPTmfLfB6aiBFpCCCGEcEFVGYVUUcpZ7pjNZv766y+Cg4M9mmNDCCFE5aeqKtevX6dBgwb51ufyNukDIwr1119/ER4eXtbFEEIIUYGcOXOGRo0aleg1qkofmIpSznLHOovnmTNnynUboRBCiLKXmppKeHi47d4hPCcBTDFZm41CQkIkgBFCCOGS0uhyYNCDjweXMaiAyWvFKTESwAghhBCViMEAhioQwMhMvEIIIYTwilmzZqEoCuPGjSs03apVq2jdujX+/v506NCB7777zu1rSQAjhBBCVCI+es+34ti5cyeLFi0iKiqq0HTbtm1j6NChjB49mj179jBo0CAGDRrEwYMH3bqeBDBCCCFEJWIweL65Ky0tjWHDhvHhhx9So0aNQtPOmzePu+++mxdeeIE2bdowffp0OnfuzLvvvuvWNSWAEUIIIUQ+qampDltWVlaBaceOHUv//v3p06dPkflu3749X7p+/fqxfft2t8onnXiFEEKISsRHDz4eVE/4mLXHvHOdvfLKK0ybNi1f+pUrV/Lbb7+xc+dOl/JPTk6mXr16Dvvq1atHcnKyW+WUAEYIIYSoTPR41r5iGcGUd54zPz+/fEnPnDnDs88+yw8//IC/v78HF3WfBDBCCCGEyMeVec52797NhQsX6Ny5s22fyWRi69atvPvuu2RlZaHXO/YKDgsL4/z58w77zp8/T1hYmFvlkz4wQgghRGVi8MLmot69e3PgwAH27t1r226++WaGDRvG3r178wUvANHR0WzcuNFh3w8//EB0dLTbb1MIIYQQlYUBz6onzK4nDQ4Opn379g77AgMDqVWrlm3/8OHDadiwITNnzgTg2WefpWfPnsyZM4f+/fuzcuVKdu3axQcffOBWMSWAEUIIkc/ly5f56KOPMJlM6HQ626YoisPrm2++mVtvvbWsiyvslWIA44rTp087rMDdrVs3VqxYweTJk4mLi6NFixasWbMmXyBUFEVVVdW7Ra0aUlNTCQ0NJSUlRdZCEkJUOh999BGjR4/GYAgCVEBFu12oqKoZUDGbjURF3cS+fXvKtrAVQGncM2zXiIAQDwKYVDOEnqTc39+kBkYIIUQ+RqMRUDAaJxSS6n+YTJmlVSThKh3aSKRKTgIYIYQQ+ZjNZhRFR+F19KrTTpqijBnwLIAp+QWzvUJGIQkhhMjHZDJR9J3MjE4nAYwoG1IDI4QQIh9Xa2AkgCmHqkgNjAQwQggh8tECmKLuZNKEVC7pkT4wQgghKqdz585x//1/IyMjwzIkWo9Op8Ng0B6Tk5Nw5av43r2/0ahREwwGA3q9HoPBkGfT9vn4GGyPPj4+6PV625Bs+8e8w7Tzvi5oX0hICC+//DK+vr4l/+GJckECGCGEqIKOHDnCr7/+AnRAuxWYAROQbXkeCHQvIpdbyckJ4dw5bVi1dp79ZgJy8uyzTweKoto9WturVLTKH9Xy3H5/7vPc4d3ZGI2XGTRoEJ06dSrW51GpSBOSEEKIyspsts5W1huoXsxc6lu24rP2sfFsRrIkYBEyrZmFnipxd5dRSEIIUQXlBjAV5Ou2EHmUaQCzcOFCoqKibCteRkdHs3btWtvxxMREBg8eTJ06dQgJCeHBBx/Mt4JlXtevX2fcuHE0adKEgIAAunXrxs6dOx3SqKrK1KlTqV+/PgEBAfTp04c//vijRN6jEEKUR9owaZAAphLSe2GrAMo0gGnUqBGzZs1i9+7d7Nq1i169ejFw4EAOHTpEeno6ffv2RVEUNm3axM8//0x2djYDBgyw++aQ3xNPPMEPP/zAJ598woEDB+jbty99+vTh3LlztjRvvvkm8+fP5/3332fHjh0EBgbSr18/MjNlRkkhRNUgNTCVWCmuRl2Wyt1aSDVr1mT27NmEh4cTGxvL1atXbWsxpKSkUKNGDb7//nv69OmT79yMjAyCg4P56quv6N+/v21/ly5diI2N5fXXX0dVVRo0aMDzzz/PhAkTbPnWq1ePpUuX8vDDDzstV1ZWFllZWbbXqamphIeHl/u1IoQQwpmvv/6agQMHAhOAoLIujoe0PjC7d++mc+fOZV0Yp0p1LaSeEOJBEJJqhNAtshaSy0wmE6tWrSI9PZ3o6GgSExNRFAU/Pz9bGn9/f3Q6HQkJCU4DGKPRiMlkwt/f32F/QEAACQkJAJw4cYLk5GSH80NDQ7n11lvZvn17gQHMzJkzefXVV73xVoUQoszl1sBUnq6QffvejY+Pj204NmiPer0eX19f/Px88fX1w8/PFz8/P/z9tUcfHx+nm06nw9/fn+eff566deuW9dsTeZR5AHPgwAGio6PJzMwkKCiI1atX07ZtW+rUqUNgYCAvvfQS8fHxqKrKxIkTMZlMJCUlOc0rODiY6Ohopk+fTps2bahXrx6fffYZ27dvp3nz5gAkJycDUK9ePYdz69WrZzvmzKRJk3juuedsr601MEIIURFVrj4wdYG7uHzZWkuu5nm0Dum2bmlAiuW5GUXJv1mHeufkJBMVFcWwYcNK7d14rAI1A3mizN9iq1at2Lt3LykpKXzxxReMGDGCLVu20LZtW1atWsWYMWOYP38+Op2OoUOH0rlzZ3S6gr8xfPLJJzz++OM0bNgQvV5P586dGTp0KLt37/aonH5+fg61QUIIUZFVrj4weoqes6ZgqlrQMO5sIL7Qe0655Olq1AV3My1XyjyA8fX1tdWOdOnShZ07dzJv3jwWLVpE3759SUxM5NKlSxgMBqpXr05YWBhNmzYtML9mzZqxZcsW0tPTSU1NpX79+jz00EO2c8LCwgA4f/489evnzl9w/vx5OnbsWHJvVAghPPTtt9/yj388haqqKErhs9Tq9TrLox6dTo/BoEev11tmy9Vz+fIlS66VIYApKVpUU+ECmCqizAOYvMxms0NnWYDatWsDsGnTJi5cuMB9991XZD6BgYEEBgZy9epV1q9fz5tvvglAZGQkYWFhbNy40RawpKamsmPHDsaMGePdNyOEEF60Z88ekpIuYDZ3texRC9nAceZbo91z6+OtgEy9X7AKGsB42oRUrob2FKxMA5hJkyYRGxtL48aNuX79OitWrGDz5s2sX78egCVLltCmTRvq1KnD9u3befbZZxk/fjytWrWy5dG7d28GDx7M008/DcD69etRVZVWrVpx/PhxXnjhBVq3bs2oUaMAUBSFcePG8frrr9OiRQsiIyOZMmUKDRo0YNCgQaX+GQghhKvMZjN6fQBmc++yLkoVIQFMeVamAcyFCxcYPnw4SUlJhIaGEhUVxfr167nrrrsAOHr0KJMmTeLKlStERETw8ssvM378eIc8rE1MVikpKUyaNImzZ89Ss2ZNHnjgAWbMmIGPj48tzYsvvkh6ejpPPvkk165dIyYmhnXr1uUbvSSEEOWJNuuFNPmUngoawFQR5W4emIqiNMb0CyGEvalTpzJr1rvk5Dxb1kWpItKB2axZs8YyZ07xleo8MIMgxKfI5AXnkwOha2QeGCGEEF6ijRySGpjSU0FrYKpIE1IF+6kIIUTVJU1Ipa2CBjBVhNTACCFEBSE1MKWtggYwejy7u8s8MEIIIbxJApjSVoEDGE8msqsgq1FLACOEEBVE7uy5onRU0ADG0z4wFeTXrIL9VIQQouqSPjClrYIGMFWE1MAIIUQFIU1IpU0LYLSVrSuQKlIDIwGMEEJUENoK0lIbUHq0O7n9RKgVggQwQgghyhOpgSkN54FUtM85BaiAAUwVIQGMqDqMRoiPh4QEiImBuDgwyJ+AqDgkgCl5Ot1SzOYMh33WBYUrDB2ejSSqIJV88t9bVB3x8TBtGqgqbNig7Zs6tUyLJIQ7JIApeaqaTXx8PMOGDcNsNhMQEEC9evXKulju8bQJyeStgpSsChJnCeEFCQla8ALaY0JC2ZZHCDeZzWZk9bqSpapmatWqRePGjYmIiKh4wUsZWLhwIVFRUYSEhBASEkJ0dDRr164tMP3SpUtRFMVhK85iylIDI6qOmBit5kVVQVG010JUIFoNjHzvLDkqoFb8YdOlXAPTqFEjZs2aRYsWLVBVlWXLljFw4ED27NlDu3btnJ4TEhLC0aNHba+LM9JLAhhRdcTFaY/2fWCEqEC0UUii5GjVW3p9BZmKtiBemok3NTXVYbefnx9+fn75kg8YMMDh9YwZM1i4cCG//PJLgQGMoiiEhYV5UEgJ5UVVYjBofV6+/157lA68ooLRmpCkD0zJkYnr7IWHhxMaGmrbZs6cWeQ5JpOJlStXkp6eTnR0dIHp0tLSaNKkCeHh4QwcOJBDhw65XT75Dy6EEBWEdOItadoEKBW+BsZLTUhnzpwhJCTEtttZ7YvVgQMHiI6OJjMzk6CgIFavXk3btm2dpm3VqhUfffQRUVFRpKSk8NZbb9GtWzcOHTpEo0aNXC6mBDBCCFFBSABTfIqyDoPhNKBYarEUy3OttkV7VDGbK0ENjKerURu1B2unXFe0atWKvXv3kpKSwhdffMGIESPYsmWL0yAmOjraoXamW7dutGnThkWLFjF9+nSXiykBjBBCVBDShFR8BsMfdOjQhFtvvRWTyYTZbHb6aDB0I6aid/D3tAamGOf6+vrSvHlzALp06cLOnTuZN28eixYtKvJcHx8fOnXqxPHjx0u6mEIIIcqC1MB4QuWee+5x6xu+KD6z2UxWVpZLaU0mEwcOHOCee+5x6xoSwAghRAVhMplkHphiqwTDo13lpVFIrpo0aRKxsbE0btyY69evs2LFCjZv3sz69esBGD58OA0bNrR1An7ttde47bbbaN68OdeuXWP27NmcOnWKJ554wq3rSgAjhBAVhNaEVEVuwl5nrvidc11Vyk1IFy5cYPjw4SQlJREaGkpUVBTr16/nrrvuAuD06dMOwePVq1f5v//7P5KTk6lRowZdunRh27ZtBXb69VIxhRBClBWtCUkUh6pWoRqYUrZ48eJCj2/evNnh9dy5c5k7d67H15UARgghKgjpA+MJqYFx6/wKoIIUUwghhNFoQgKY4qpCNTBVZDXqClJMIYQQZrMJ+bddPKpahWpgqgipgRFCiArCZJImpOKrQjUw0oQkhBCiPNEWc5QApjiqVA1MFQlgqkg4KoQQFV9FCWB8yWEjS7nIG2xkKb7klHWRqFI1MFWE/DSFEKKCqCijkNbyKXdyktpkcCcnWcunZV2kqlUDo/fCVgFUkIoiIYQQFaUPTBTnbaVULK/Lmqqaq04NjDQhCSGEKE8qShPSfuphXfFAtbwue2rVqoExeLBVkI9JAhghhKggtCak8v9vO5Zh/EgElwjgRyKIZVgZl0ibwbjK1MBUERWkokgIIYTWhFT+ZeNDb0aWdTHsaPVBVaYGpoo0IVWQYgohhDCZjFSEGpjyRwtgqkwNTCmvRl1WqshPUwghKr6KMgqp/KliNTBVRJkGMAsXLiQqKoqQkBBCQkKIjo5m7dq1tuOJiYkMHjyYOnXqEBISwoMPPsj584X3ZjeZTEyZMoXIyEgCAgJo1qwZ06dPR1VVW5qRI0eiKIrDdvfdd5fY+xRCCG+oKJ14y58q1gfGkw68njY/laIyLWajRo2YNWsWLVq0QFVVli1bxsCBA9mzZw8RERH07duXm266iU2bNgEwZcoUBgwYwC+//FLgL+Ibb7zBwoULWbZsGe3atWPXrl2MGjWK0NBQnnnmGVu6u+++myVLlthe+/n5leybFUIID1WUYdTlTxWrgZE+MCVvwIABDq9nzJjBwoUL+eWXXzh37hwnT55kz549hISEALBs2TJq1KjBpk2b6NOnj9M8t23bxsCBA+nfvz8AERERfPbZZ/z6668O6fz8/AgLC3O5rFlZWWRlZdlep6amunyuEEJ4g1YDU0VqEbyqivWBqSLKzU/TZDKxcuVK0tPTiY6OJisrC0VRHGpG/P390el0JCQkFJhPt27d2LhxI8eOHQNg3759JCQkEBsb65Bu8+bN1K1bl1atWjFmzBguX75caPlmzpxJaGiobQsPD/fg3QohhPukD0xxVbEaGB2ezcJbbiKDwpV5RdGBAweIjo4mMzOToKAgVq9eTdu2balTpw6BgYG89NJLxMfHo6oqEydOxGQykZSUVGB+EydOJDU1ldatW6PX6zGZTMyYMYNhw3LnIbj77ru5//77iYyMJDExkbi4OGJjY9m+fXuBv+CTJk3iueees71OTU2VIEYIUaqkD0xxVdE+MJ6cXwGUeTFbtWrF3r17SUlJ4YsvvmDEiBFs2bKFtm3bsmrVKsaMGcP8+fPR6XQMHTqUzp07F/pL+Pnnn/Ppp5+yYsUK2rVrx969exk3bhwNGjRgxIgRADz88MO29B06dCAqKopmzZqxefNmevfu7TRfPz8/6ScjhChTUgNTXFWsBqaKKPMAxtfXl+bNmwPQpUsXdu7cybx581i0aBF9+/YlMTGRS5cuYTAYqF69OmFhYTRt2rTA/F544QUmTpxoC1I6dOjAqVOnmDlzpi2Ayatp06bUrl2b48ePFxjACCGEq/744w8uXLiAoijodDrblve1Xq93eF3Yfr1ej9GYQ4Wp3y9XqlgfGKmBKRtms9mhsyxA7dq1Adi0aRMXLlzgvvvuK/D8Gzdu5Psl1ev1lm8uzp09e5bLly9Tv359D0ouhBCQk5ND+/YdyM7OKjpxsbQsoXwrsypWA1NFJrIr0wBm0qRJxMbG0rhxY65fv86KFSvYvHkz69evB2DJkiW0adOGOnXqsH37dp599lnGjx9Pq1atbHn07t2bwYMH8/TTTwPayKYZM2bQuHFj2rVrx549e3j77bd5/PHHAUhLS+PVV1/lgQceICwsjMTERF588UWaN29Ov379Sv9DEEJUKkaj0RK89EELNlRwWNqwsM3swrGCa6BFQaQPjNvnVwBlWswLFy4wfPhwkpKSCA0NJSoqivXr13PXXXcBcPToUSZNmsSVK1eIiIjg5ZdfZvz48Q55WJuYrBYsWMCUKVN46qmnuHDhAg0aNODvf/87U6dOBbQIfP/+/Sxbtoxr167RoEED+vbty/Tp06WPixDCY7mTZoYAdcuyKMKmitXAVBGKaj9FrXBZamoqoaGhpKSk2OapEUJUPMnJydx88y2kpl637VMUx46y1hm7rR1oc1/bBywq2lOVa9euAEOAdiVdfOGSy8ACNm/eTM+ePcukBKVxz7BdYzOEBHmQTxqE3kG5v79VkIoiIYQoGUlJSZw7dxboilZr4ozq5LmK8xFBClongmZeK6PwlHTidfv8CqCCFFMIIUpG7k3tJqBhWRZFlBitD4w0IVUuEsAIIaq03JuatKZXXlWsBkZGIQkhROWXe1OTAKbyqmKdeKtIE1IVCUeFEMI5CWCqgipWA1NFVJA4SwghSkbuTa3gyS5FRVfF+sDo8ezuXkE+JglHhRBVmtTAVAVVrAbGk5Woi9F/ZuHChURFRRESEkJISAjR0dGsXbu20HNWrVpF69at8ff3p0OHDnz33XfuXRQJYIQQVZx04q0KqlgfmFLWqFEjZs2axe7du9m1axe9evVi4MCBHDp0yGn6bdu2MXToUEaPHs2ePXsYNGgQgwYN4uDBg25dVyayKyaZyE6IyuHUqVNEREQAjyFzt1RWp4GPOHz4MG3atCmTEpTqRHaHICTYg3yuQ2g7OHPmjENZ/fz8XJ6xvmbNmsyePZvRo0fnO/bQQw+Rnp7Ot99+a9t322230bFjR95//32Xyyk1MEKIKk2akKqCKtYHxuCFDQgPDyc0NNS2zZw5s8hLm0wmVq5cSXp6OtHR0U7TbN++nT59+jjs69evH9u3b3f7bQohRJUlAUxVUMX6wHhpGLWzGpiCHDhwgOjoaDIzMwkKCmL16tW0bdvWadrk5GTq1avnsK9evXokJycXp5hCCFE1SQBTFUgfmOKwdsp1RatWrdi7dy8pKSl88cUXjBgxgi1bthQYxHiDBDBCiCpNApiqQGtCqio1MKoOVA9iNbUYH5Ovry/NmzcHoEuXLuzcuZN58+axaNGifGnDwsI4f/68w77z588TFhbm1jWrxk9TCCEKIOMYqoKqVQNjMni+ecpsNpOVleX0WHR0NBs3bnTY98MPPxTYZ6YgUgMjhKjSsrOzLc+qxs2taqpifWBK2aRJk4iNjaVx48Zcv36dFStWsHnzZtavXw/A8OHDadiwoa0T8LPPPkvPnj2ZM2cO/fv3Z+XKlezatYsPPvjAretKACOEqNJKJoDJQWu2UOw2nd1zUbqqZg2MJ+e748KFCwwfPpykpCRCQ0OJiopi/fr13HXXXQCcPn3aIXjs1q0bK1asYPLkycTFxdGiRQvWrFlD+/bt3bquBDBCiCott5rbW/8Or6Mo81BVYyFpFBRFh6LobM+twY2i5AY79s8L3rTjWkuYNS2oqvWY9lx7bX3E5Xy9c8wA3AQEOPkssoEzts/FeR7O8nQnjRakVpUaGKNewagvfqBs1Ku40yds8eLFhR7fvHlzvn1DhgxhyJAhbpbMkQQwQogqzfs1MOmoqpHXX3+dyMhIzGazbTOZTE6fF/XaW2nzHjMajZb91s2E2WyyPTeZrGlNtnNz0zleQ1XtX2uPqqodu349BS14ucnJ57UbWO+lz75wrk7CJioGCWCEEFWa9wMY7Zvr3XffTZcuXbyUZ8WVlZWFv79/ISmyqV69Jnv3/mYXJOUPuop6LCpNnTp1qsys6SaDAZOh+DUwJoOK1gxavkkAI4So0rwfwFSxWV+LYDKZLM8KuqGq+Pj40qRJk9IqUqVn0usxedCEZNJXjACmajQICiFEAXJvsN76d1i1OowWxWw2W54VHMDodNKxWbhPamCEEFVa0TdYt3MEJICxyv18CwoQ1SrTuba0mNFj8uD32VxBJnWUAEYIUaV5P4CRGhh7RTchmdHp5LPyJiN6jB78PhslgBFCiPJPamBKlmtNSFID400m9Jg8aBI1YS46UTkgvzVCiCpNamBKliudeCWAEcUhNTBCVFFGo5GffvqJrKws23pAzh7z7mvRogVt2rQpgxKXDKmBKVm5n+8BIIn8k86dQ6/3LaPSVU6e18BUjE7VEsAIUUV99913DBw40O3zmjZtQWLisRIoUdnw/mKOsu6OverVq9OxY2eSk5Mxm6/YTYCXO3/LLbfcU9bFrFQkgBFCVGoZGRmWZ3NwnOJdyfNov/9rbtzYV9JFK1W5gYZ3AxltGQDh7+/Pnj27y7oYohKSAEaIKiq35qEmzteoccYPb9/oy5r3A5jK9fmIikdqYIQQlVpuAOPOPyulBJpcypbUwIjKxoQeYxUIYKSRVghRpeUGGpUrMBOispMaGCGqqOLWpFTeGhjvkhoYUVZMGKrEPDASwAhRRUkTkqakmpCEKCsmdJg8WJzUVHSSckGakISo8twLYCqb3ADGW986JRASojRIDYwQVZQ0IWmkE6+obLRRSFIDU6IWLlxIVFQUISEhhISEEB0dzdq1a23HExMTGTx4MHXq1CEkJIQHH3yQ8+fPF5qnyWRiypQpREZGEhAQQLNmzZg+fbrDP11VVZk6dSr169cnICCAPn368Mcff5TY+xSiMpEARojyTVvM0bOtIijTAKZRo0bMmjWL3bt3s2vXLnr16sXAgQM5dOgQ6enp9O3bF0VR2LRpEz///DPZ2dkMGDDAbmrq/N544w0WLlzIu+++y++//84bb7zBm2++yYIFC2xp3nzzTebPn8/777/Pjh07CAwMpF+/fmRmZpbG2xaiXCh+DYyXC1LGpAZGVDZmDJaOvMXbzBWkcaZMSzlgwACH1zNmzGDhwoX88ssvnDt3jpMnT7Jnzx5CQkIAWLZsGTVq1GDTpk306dPHaZ7btm1j4MCB9O/fH4CIiAg+++wzfv31V0D7p/3OO+8wefJk2zTqH3/8MfXq1WPNmjU8/PDDTvPNysoiKyvL9jo1NdWzNy+El504cYKJEyeSk5PD0KFDGTJkiItnSh8YjUxkJ0RFUm468ZpMJlauXEl6ejrR0dFkZWWhKAp+fn62NP7+/uh0OhISEgrMp1u3bmzcuJFjx7S1Wvbt20dCQgKxsbGA9k8+OTnZIQAKDQ3l1ltvZfv27QXmO3PmTEJDQ21beHi4p29ZCK9RVZWtW7fy+eefs3r1Bj755BNMJlOhm9FoLMaVFMxmE5mZmWRlZZGdnU1OTg5GoxGTyVQhm5ccAxgjWmfeTLSeAPbvxwRkADfsjmNJb7KcmwNkA1IDI8qOtQ+MJ1tFUOb1RAcOHCA6OprMzEyCgoJYvXo1bdu2pU6dOgQGBvLSSy8RHx+PqqpMnDgRk8lEUlJSgflNnDiR1NRUWrdujV6vx2QyMWPGDIYNGwZAcnIyAPXq1XM4r169erZjzkyaNInnnnvO9jo1NVWCmEqkY8cuHD/+B2azmUWLFvLYY4+VdZHccscdvdi6dTPad5LmfPPNNxgMrv55u3OjPc+1a6kEBBS99ICiKJZNZ3vU6/XodHp0Op3lUXuu1+vR6/W2NFo6HTqdDpPJZLcAoONCgKqqWh61/dnZmeTkmAgI8Hf5vebkWJuOFxVxTt7gTEH7F5rj9KyGDbX/D6pqtgR2uSt7a8GN4uQzUmzH7D/DevXqcejQfpc+dyGqSifeMg9gWrVqxd69e0lJSeGLL75gxIgRbNmyhbZt27Jq1SrGjBnD/Pnz0el0DB06lM6dOxc68dTnn3/Op59+yooVK2jXrh179+5l3LhxNGjQgBEjRhS7nH5+fg61QaJy2bfvNyAGSODw4cNlXRy3JSaeADoB9wC1gVtdPLMG7v0baAJsBt4HqqHVPqh2W+5rVVVtN27rsZwcE7k1Fq48NwN6tMAs7+Zs/3vAFdLSnnLjPeUAW4BaQHXLdQPQalmyyB1e7Q8Ekxu0XEKrjfEFQuzKpACNMRp9LPsUu02jxTEqzj+7vMeOceLEKq5fvy4BjBB2yjyA8fX1pXnz5gB06dKFnTt3Mm/ePBYtWkTfvn1JTEzk0qVLGAwGqlevTlhYGE2bNi0wvxdeeIGJEyfa+rJ06NCBU6dOMXPmTEaMGEFYWBgA58+fp379+rbzzp8/T8eOHUvujYpyx2w2M3PmTK5du2bZcwkfn/oVuOq/MdDB8rx+YQk9EGx5fAAtUCpvPkcLBJ5087yxJVAWb/keWFViMwaLysfziewqRlNwmQcweZnNZofOsgC1a2v/KDdt2sSFCxe47777Cjz/xo0b+f7Q9Xq9beRSZGQkYWFhbNy40RawpKamsmPHDsaMGePFdyKcUVUVo9HI1atXSUhIYMCAAfj4+JRJWU6dOsXkyZPR6YIse7oAG4sMYHbu3MnKlSttTRhWiqKg0+kcHn18fBgzZgwNGzYsuTdCafa3sP5tZZfS9YqjogagBdH+d+n1FaNfgih7ng6FNkoAU7RJkyYRGxtL48aNuX79OitWrGDz5s2sX78egCVLltCmTRvq1KnD9u3befbZZxk/fjytWrWy5dG7d28GDx7M008/DWgjm2bMmEHjxo1p164de/bs4e233+bxxx8HtH/048aN4/XXX6dFixZERkYyZcoUGjRowKBBg0r9M6hqevXqy08/baZ582YcPXqUjz76iFGjRpVJWXx9fQEwm9OAKUA0RuN/2bBhA6+++qrTPiSbN28mNrY/OTnVLIGPtWnAWt0P9s0oOTlnqV+/PmPHludv+O6wBjDO+32UPTPlaGyCl2g9EqQGRghHZRrAXLhwgeHDh5OUlERoaChRUVGsX7+eu+66C4CjR48yadIkrly5QkREBC+//DLjx493yMPaxGS1YMECpkyZwlNPPcWFCxdo0KABf//735k6daotzYsvvkh6ejpPPvkk165dIyYmhnXr1uHvX1DHP+EOo9HI77//jqqqtg6IdevWBeDw4d8xmYwcPXoUgP/+9yuvBTDHjx/nxo0bNGjQwFZrZzKZnH5zNZlMfPXVV/To0YOffvoJSAG0Dpe//vorO3bsoHv37g7nbNq0iXvuuZecnLaYza9iMhXdJ0qn61+KI3NK4zrW2o3ijGAqDSqVL4DRfq4SwAhXWedzKf75FYOiVsRxj+VAamoqoaGhpKSk2OapqYhSUlL4+eefqVatGhkZGXlGSeQ+Oo74UB2e531888032bNnj8N1FixYQP369Xn88b+TmvoQ8BYwnPr1d/DBB+/ZmlxyR2FQ6GtFUbh+/Tp6vZ7g4GB27tzJ888/b7veypUr+e2333jzzTdp1KgRS5cuBbQmrJdfftk2L5BeXx1FCcJofA5oDYwDjvH1118THBxsu+6ZM2d44oknyclph9k8DXC1Q/ddvPjii/ztb3+zjaCxflb2zxs2bEjLli1d/8Hl0bhxU/460444AojhCAm0Jp5BJTAc8lfgbeA40MzLeXtDRyAN2FDG5fCmr4BxpKWlERgYWNaFEcVUGvcM6zU+T+lJtZDiBzA3Uo08GLql3N/fJIAppsoSwLRo0YLjx497PV9FqYeqfgWcAh6yPwKMBuajBTFTnZztiaeBd91IPx2t7wvAt2ijWJzT6bpiNr+CNurEVX1xpWYkKCiE69dT3MjXUePGTRl5xpdpHEWH1pAyjSFM54Fi5+mcNYA5BrTwct7ecBPayKAfyrogXrQaeI4bN27IKKQKTAIY7yv2O0xMTGTJkiUkJiYyb9486taty9q1a219T0TFoAUvBmAocDvaMFz7oZzWR+vwUF2eR+f7VNXfcs6twAC0Tp8q2uiYapZ8nwceI/9QXAp4nneLRhsJ87IlXTWgpmW/dW6PQLQbGuTv3GlAGzqrAiuAT4GmlvwMea6vYDbXx/3mCR3QDS2Is/+8rMcUYDOZmZ+7ma8jRVGI4bJDzjEc8SjPAq5keSzPTUiVjXTiFe7xfB4Y9/6OZs6cyZdffsmRI0cICAigW7duvPHGGw79VfNaunRpvu4Dfn5+bi3pU6wAZsuWLcTGxtK9e3e2bt3KjBkzqFu3Lvv27WPx4sV88cUXxclWlAEfHz9MpvqoahqqOpKS6RYVYNkAhz8qBaiXP7nL/IFGls1ejTyvC/sGoQLLgc/QamJmeVCevLLRWpO7oAVGBQnB05EziqKQQC36cMlWA5NAa4/yLOBKlseC1yMrW5W3D0zFHd4vSpsRnYejkNz7+96yZQtjx47llltuwWg0EhcXR9++fTl8+HChzZ4hISG2/pDg/u94se5WEydO5PXXX+e5556z9RMA6NWrF+++6071vShrM2ZMZ926dWzatBpIRavBKEkq3g2SPPmnbgLmoE3MdgsQ740C2bGO1Cn6H4k37k3xNAOiHPrAeJ+1oOW1m59WW1a5aDcT6cQrXOV5J14taM675l9BE7quW7fO4fXSpUupW7cuu3fv5vbbby/wOoqi2OZmK45i/UUcOHCAwYMH59tft25dhxFBovx74YUXymCIb3lZNG8dWvByH94PXsCVwCWX5zUwJnRM5wH68TLTeaCE1jMp7/PAVN4aGAlgRGkLDw93WANw5syZLp2XkqL156tZs/AvxGlpaTRp0oTw8HAGDhzIoUOH3CpfsUK06tWrk5SURGRkpMP+PXv2lPiEXaIs5KB1jP0cSAf6AS/h/kysWWjf3M+g9aHwRk2MJ//UDwJBwD+9UA5nrGUrqrZC9bh5QDu/NIdRl9e+JpVxHhitBkaakISrPO8Do/3OnTlzxqETryvL6ZjNZsaNG0f37t1p3759gelatWrFRx99RFRUFCkpKbz11lt069aNQ4cO0ahR3m4BzhXrDvLwww/z0ksvsWrVKhRFwWw28/PPPzNhwgSGDx9enCxFufB3tH4l9h1ys9E6uCpAQ7SOsu8A/0Yb8dMQx4ncFLTp5uui/eP9DW3kym9ogYse+AL4Gm3I6y1oHW2tNx5ftCHK/pb9jYAIy/EDwEnLNe+2lNnTf+oleVOw/nkVfbM3GrMZM2aMw3B0wDZk3f61M1euXPFKiYtm/bzKaw0MVL4mJFVqX4RbvBXAhISEuD0KaezYsRw8eJCEhIRC00VHRxMdHW173a1bN9q0acOiRYuYPn26S9cqVgATHx/P2LFjCQ8Px2Qy0bZtW0wmE4888giTJ08uTpaiDHXq1Ik+ffqRmXkRs9lxrpffftuJ0agHlgKPWs44hLYWzhvk9vMo6Ju5Hm2BvCbAncAyIAgCe0PmbjAvBdW+hsJM7qgkZzUXBrTaG188HwlzGi1IKmst0OmasXjxBkBnWejPcfG//K8dGY3XSrKAdqw30vJcA1M2S1OUHDOVLygTldHTTz/Nt99+y9atW12uRbHy8fGhU6dObk3rUawAxtfXlw8//JApU6Zw8OBB0tLS6NSpEy1alMd5IURRIiMj+eGHdU6PPfjgg3zxRQqq+qjd3nZQ6BDdC2jzhJjRhhDb/5r9AP4RUH9N0QUz34DMXZC1E1AgMBZ820Dmb3BlMtz4nuL/Yz8KnADy9+XyngzLY1F/Zm0wGud5eK3BuNfnprjK+zBq6+rVlYnnTYyiajF5uBaSyc1RSKqq8s9//pPVq1ezefPmfN1LXLqmycSBAwe45557XD7Ho04IjRs3pnHjxp5kISo4PUbiiCeGBBKIIZ44TNRFa0IqiPbPWK8aibsaT0xGAgkBMcTXiMOk2P1K6qpBtdu1zZ5/Z2jwHfxZE8zF/cf+MVpTVUk2eV6zPLo6a69n9KjE8d8SnonXnfI4+90o6dVLJIARwlujkFw1duxYVqxYwVdffUVwcDDJyckAhIaG2iZfHD58OA0bNrR1BH7ttde47bbbaN68OdeuXWP27NmcOnWKJ554wuXruvwOn3vuOZczffvtt11OKyq2OOKZxjR0qPSxTN8+vdDZdfWgat/e467GM+2K5dwMy7k13ZmZt7hDZv8E9gAD0TrxlhRrDUzpNGnEccw2E28fDgCUwEy8VkV/Q3P/d8MbTEgAI0TpWrhwIQB33HGHw/4lS5YwcuRIAE6fPu3Ql+vq1av83//9H8nJydSoUYMuXbqwbds22rZt6/J1XQ5g8q5t89tvv2E0Gm0z7R07dgy9Xk+XLl2cnS4qqRgS0FmHeaISQ+EdtyAbFK1GIiYjz7kZRZ3rTHH+se9C68vxZDHOdYe1maU0OmCqxHClFGfiLfobmvu/G95QOWtgpA+McIcJnYedeN2b58mVFYk2b97s8Hru3LnMnTvXrevk5XIA8+OPP9qev/322wQHB7Ns2TJq1NBmPb169SqjRo2iR48eHhVIVCwJxNCHDehQMaOQQEwRZ+T+M04IiKFPht25AUWd6y2GPI8lfZ3S6PBqIoGa9OFiCc/Ea1X0DdX93w1vqIzDqKUGRrjH81FIFeNLQLH+g8+ZM4fvv//eFrwA1KhRg9dff52+ffs6rAosKoOCb8DxxAE49HMonA/WkUvxNSzn2vWB8WbZChaKdqNLIf+yA95k/SdQGgGMSjytKfmZeK1+AFqSfxmHXO7/bniDt2tgPsPHZym5o8ByN7PZB5PpdbRO7SVLAhgh8itWAJOamsrFixfz7b948SLXr1/3uFCi4jBhcLNfQ5atCcmkGNzs8+Itv6D96oeWwbVLit7ysyipPi9W1qVDZlk2HVonZetCmvXR5u1piYlOTOcfeH/F8aJ482a/nbp1b/DAA/c7zMljNpst7f4HkQBGlDdSA1OIwYMHM2rUKObMmUPXrl0B2LFjBy+88AL333+/VwsoKpuynup9J/Cz5fl/gSEleC3rn1dprBtUWvOfNAbeB84Dly3bFcvjRWAvsDXPOXq0iQmD0Fb/boi2uGVL4CagE95bg0vBuwtNqrRo0Yp58xyHuZtMJksAUxqBhfSBEe7xfBh1JQ5g3n//fSZMmMAjjzxCTo7WHGAwGBg9ejSzZ8/2agFFZePNG4y7wVAmMB8Ca0Od5nDyA0seD9ql2YT2rVpP/qYfFceyO5toTrF7fc3yvDRWbjYApVX7Wd2yFcSIFtRcxDHIuWTZdwKtCcqeAa0mJxioQ+6sz43RlqxQnWw4ef6XZZtTjPeVv5kI/kBR8jeT5XZaLJ0O2kK4w/Nh1OV1tXlHxXqH1apV47333mP27NkkJiYC0KxZs0KXzRZCU1pr9jjzH+AK/N8P0OpOmH07/Pmh5diDwEpgMegCQMlzYzJlWZ74O8nX2ftRKd3J3rxd8+AJAxQ5F1AWjrU3l9ECHGtNzh9oAaeOGjVqAQqK4nwDaxOLwvnzCqqqUr/+N06uWfDvnapaZ592XL5BVVW6dh3kJL01r8PA92i1bCa0n0E7oFkh791d0olXCGc8GoYRGBhIVFSUt8oiyim9amYSr3lpQjIjJdnUocdEHP8hhkMk0I54HrJUh54EVkGL26FNby3xC1vtgpj/AKlQtwfEboa8a8+sjoIUM+gPul4WUyJxanNiWEECp+3KUlIq0jd1P6CBZSvIu7Rrl8rBg/tcy9JohPh4SEiAmBiIiwNDyYw00+l0hIbWICVlCbAkz7FumM2fevFqEsAI90gfmELceeedhf5Bbdq0qdgFEuVLZmYmk9jMNDZZJiT7ATho6SyqQ/smnWZ5bkRbrToLrUYgCK3Gohra2kX+wFXIPgI3fgRFj8NCkKp1HaTC1v2xpFezwZxG3pt2HP9hGp9ayroXgOk8BMwFvS889XVuYp1OC2LGGIBUqHe7Frw4/d12vx9CHO8xDdBxkj6cspTlEbfycE9luMndQGt602qU3FrEMD4epk0DVYUN2sR5TC2ZDsR6vZ6zZ0+TlpaGXq+3bf/4xz/4/PODwO9ov8v2NTPWRytnTVZ5Nx2QLAGMcIvn88BUjKkIihXAdOzY0eF1Tk4Oe/fu5eDBg4wYMcIb5RLlxLFjx3ja7tdZmyBtFbDKSWoFre+INbX1n3cepjPwVy8vlM66YnauGA7lmTztEJAA/AFDFkC1PCurHv0RUKH1WLhtQQHBS/HEqL/afW7WslRmJ9E68IaS20+mOtpQ9SBc6S+i10/GZPrL9jooyI25YxISwNq0o6ra6xIUFBREUJDjTM4hISGYzQcB19dzcUVIiHsL4wlRFRQrgClo9rxp06aRlpbmUYFE+dKhQwcS/rxCn5xLuROSNRgPjZ4HNQd0gWComb/Jxcps1mpKTGlgTIHf2kJoS+i1EEw5YDaBzvJNQTFYntt/S7Xma9lnNmn7DP6wuk++bh8JtKMPe+0mTwsDPoZqteDOp/OXL1Vbs4MWo7wavAAkKDfTR02wTCynkFDiw23Lug/Mz8B3BAdX5/r1aw5HFEWPwVADVa2O0Wgf4NSwbHWBBijKDUaPHs1jjz2Gqqq0bNnS9cvHxGg1L6qq/SxjSmtixFxvvfUWI0eORKfTOdTM2G+KouTrZ5N3s18R3mw2U79+/VJ/L6LiMno4CsmTc0uTVxuIH330Ubp27cpbb73lzWxFGVIUhZlBN0FwD2JSE0gIiSG+cZwWbLhCpwNdCBhCwK8BGGpBcDg08kINjN4332KO8TwAXCWGvSRwg3jWgs4PRn3hPA9bZ0xXghf3Apx4ngTeIYZIEuhOPA+5db57rE14ZatJk2acPHmczMxMkpOTSU5OJikpKd925sxfJCUd5MqVi5jNWuCl0/ljNGYSGRlJz5493b94nGWiPPs+MKUsODiY6OjoUr+uEPY8H4VUGlM/eM6rAcz27dvx93c2SkNUZCZFx/QmXupLoOjB7M0brX1QsQ4Ti5lOurY/uB50nwj9XwHfAn4v9ZYOxWpRf7Du94ExKf5MVwEeAm4vIrU3lH0/CevoHH9/fyIiIoiIiCg0vclk4vz58xw7dozt27ezceMmunfvXryLGwwl1udFCFH+FCuAyTtZnaqqJCUlsWvXLqZMmeKVgonKypx/iHJxqfZBxX+BxVCnBfQZB1H3Qs3G3rkOWDoYu1utal2moLRqRsp6FJLi0qJu9vR6PQ0aNKBBgwbccccdTJo0qYTKJkTVYfZwFJK5MjchhYSEOPSK1+l0tGrVitdee42+fft6rXCiMvL2PDAqsAZYDJHRMHFb8bIpkVEepd0npaz7wCi25iAhRNmRYdSFWLp0qZeLIaoMxQDmHM/zSU8GYzraJGJAo47wYjFGneRkao87J0DNTnaBjN1QbkWBlN+158ZbcvfbhrnqAINdzZIBaAPqNcvrJcCfOM4am5f9MWu+zuicHLNOmld+mpCEEKKkFSuAadq0KTt37qRWrVoO+69du0bnzp35888/vVI4UQkpOlvHWV9jJmu/voeoS/vYX/smYu/7jmyD1ldFbzYStyuemL8SSGgQQ/zNcZh0Bjj9A/pD/ybuz9XEYCYhsBbxD76D6bZHi1ceY5bWL+fCNri0y1JG8sQZ9i+S7fZZ95sBo+WlirbatmUeEnRo85qsLaQQBc0740xB+034YmIt04niFPtpQiwvkY1vIdf1Np0EMEKUAzIPTCFOnjyJyZS/02NWVhbnzp3zuFCiMsuNDtZ+fQ93nvsRBbjz3I+s/foeet+vTYIYtyueaTumaRPSndGCgemKHn6ZTBwK01DRAX3Sr8ClP5le3OL4B2sdeB/YAXW6FJxuZVu4CvCdixmnANlAV2AMMK/w5B6rwVr2cydXtc+TQ6zlDXpTun3SJIARouwZ0aOXYdSOvv46dxbT9evXExoaanttMpnYuHFjkaMORMWSmZmJenUznJhkN1MullE71sUU89RGqJY0ioF8s+pmnYUrqbAtjqjkXxwaY6Iu5U4ZH/NXguOEdCe+hQu7wMePmJwsxwnijnswYZm12celG687N+dQtFmJobSadqK47vh5Wmb/LT06VFX6wAhR1jwfRl0yS3B4m1ulHDRoEKDNDZJ3xl0fHx8iIiKYM6c4q8CK8urYsWPatP1nZ3kv0+xU2D2T/cCd5NbJ7K99ky1JQoMY+pzZkDsh3ZXDEFQd/nuWhJkj6bN5Ve4Ecc09mLDMZOmPU9TIKIcRT64qzdoIHfsJ4k6u5X6eNCnF6wP4cP78X4SHN2HkyOEMHTqUtm3blnIZhBBVhVsBjHWEQWRkJDt37qR27dolUihRfrRv355jxwJQ1R8se+wXFbCugOxs/RbQOpZaa2est9UGENQB2n1LbPp+1v4+hCjTNfYrOmJ9Q+DiXqjTkfibtUnIYv5KIEHnS/yp/0HcCvCvRvyUFVC7ATH/fZcEHz/iuzxY/DdotjSFKq5UmRa3JqU02pN1xNKetVx16ANTunoDlzh79jTx8W/y+uuv079/fzp37swjjzxC69atS7k8QlRNMoy6ECdOnPB2OUQ5pS2mV9uyeUMw6ALAty7Zvn3o3e0qpPys1fCc+ApOfA2hTTH1mM/0rlMh9RR82hYat4Hu9wFgMhiY/s934N7/gydvgXl94YWfoFYxahzcavJwt0bFnVl+PZeNrtT7vDgKBp4AwGzOAX5k7dodrF27idWr17Bx4wbq1q1bhuUTomqQYdR5zJ8/nyeffBJ/f3/mz59faNpnnnnG44KJ8kFRFBTFxS4iLtFpTVL2QrtD6DeQfREuLoczb8C390K1enDjEigqvLUuf1aR7WDej/BMT5gdAxN+gtoR3iqoo2LNE1O6AYxn0tDrX0Wny3RyrLjLLGj7cnIyOHjwALNmzeLtt9/2pJBCCGHjcgAzd+5chg0bhr+/f4GLOYJ2w5MARhTsBphvOD/kWwcajof6T8Gx/4OLKwAT+AZCrQbOz2l7K8zfAs/cAXN6wsQdEBrmenGsfV+KClCK1QemNHlavsuYTGcYMeJxGjZsmJurk8g17z5X0mRkZDBq1CgPyieEcJURPToZhZTLvtnIW01ICxcuZOHChZw8eRKAdu3aMXXqVGJjYwFITExkwoQJJCQkkJWVxd13382CBQuoV69egXlGRERw6lT+0RdPPfUU//rXvwC444472LJli8Pxv//977z//vteeV+Vjzc7o/qAoYjmqMyTcOlzoB4wCrLiYUQ7WHZIW+8mr7a3wrxN8PTt8FYPmLAVQt1cvffzm6DDsxDzDqSehE8jC0j4P6C/i5laP7fSmlPB8wDr73//O127dvVCWYQQZUVrQvJkFFIlC2Dsvfbaa0yYMIFq1ao57M/IyGD27NlMdXFBtUaNGjFr1ixatGiBqqosW7aMgQMHsmfPHiIiIujbty833XQTmzZpc4NMmTKFAQMG8Msvv1j6ZuS3c+dOhzlqDh48yF133cWQIUMc0v3f//0fr732mu113vciNEpJTLFvdtZMYZG6Aw72Q6/6Ekc/YviRBO4i/uz3mEa2h6UHCU27xl9DwgnIziTD158Gq86Q0qglGHyoc+E4f73YAD3aIpQNpp/iYp1GAOhNRuLWxhNzPIGE5jHEx8ZhumSZdDEoHDIvwqV9Wr+YARtxCNzW3gfGG0AWsBBtQjvr75n9Z2Q/kNla07QU2Icja98b+yHoqpN9efkB1ezSWaUDmwFr7UlDoHMBeQghRMWnqMWYeUqv15OUlJSvQ97ly5epW7eu00nuXFWzZk1mz55NeHg4sbGxXL16lZCQEABSUlKoUaMG33//PX369HEpv3HjxvHtt9/yxx9/2G7Gd9xxBx07duSdd95xuVxZWVlkZWXZXqemphIeHk5KSoqtfJXRww8/zKpVFzGbN3opx3oQ3Bw6/pz/0Jk34WQcEMoU+jCNVbZh1NPow3Q2QKMWpF84TUB2pm1cU4aPH4GNW8GpwxiNRtv4KBUtiPF5X/t9nPLta0z7Zpotz7jYSbyxbQmkJLn5HgyAP46Bi7M/oxzL/my0hR0Vu7Q6HIMd7J4rTvZZH29Y8su73zrnjA4tsPJHC5xcdQp4iR07dkgNjBAlIDU1ldDQ0BK9Z1iv8UjK2/iGBBQ7n+zUDFaEPlfu72/FqoFRVdXpN/N9+/ZRs2bNYhXEZDKxatUq0tPTiY6OJjExEUVR8PPzs6Xx9/dHp9ORkJDgUgCTnZ3N8uXLee655/KV99NPP2X58uWEhYUxYMAApkyZUmgtzMyZM3n11VeL9d4qMu/XwKigK2B6+9OvAZ2A94nhGceJ7EgHZsHZiQSgOty6A3Ky4NRh2n82Ad2QWQ7H9HajjGKOO06Ot3vbEmh5B+z8DKrXh/AO0O0RMFtWj1Z02nbxJHw5DfAF9oLSxsW3+gfQEvgEuM+1c4rtDqidBhOOweK+8EcxF7UUQlR4MgrJiRo1alhGpSi0bNnS4eZmMplIS0vjH//4h1sFOHDgANHR0WRmZhIUFMTq1atp27YtderUITAwkJdeeon4+HhUVWXixImYTCaSklz7xrxmzRquXbvGyJEjHfY/8sgjNGnShAYNGrB//35eeukljh49ypdffllgXpMmTeK5556zvbbWwIhiyL4AJ18GVAjuBrXuBbMZzBlAPyCEBG6mD9tzJ7LjZmAwYCCD5wkgt5YlA2i3/Dnq/S0Gs05BZ1Zza2AADmsLPiaE1qcPCjpUVgD7zCa4Y6wWwFSrDg3bwO0j8pYWljwFBl8wtQVcDF4clEYfGHPudVQVrUbmNPmbp/I+NwE+gLu1UEIIUbbcCmDeeecdVFXl8ccf59VXX3VYSsDX15eIiAiio6PdKkCrVq3Yu3cvKSkpfPHFF4wYMYItW7bQtm1bVq1axZgxY5g/fz46nY6hQ4fSuXPnAvu/5LV48WJiY2Np0MBxBMuTTz5pe96hQwfq169P7969SUxMpFmzZk7z8vPzc6gNqlrcaWVUgcNoTR3WlZPtm0QuQsZFOHM495TWqyDkdrSbcHUA4tEC4Rh2kcDNttcwgAZ8yV/8TABa8HLXoqcJe+h2AIac+ohVTR5HZ1YxAQ0A5vWz5KlpCfwDyHjkPfC11LoV1JKanQHbVkBACKS5+62kNKfVN+WOpPIJtOx70e1cgoKCvFckIUSZkInsnLAuHxAZGUm3bt3w8fHxuAC+vr40b94cgC5durBz507mzZvHokWL6Nu3L4mJiVy6dAmDwUD16tUJCwujadOmReZ76tQpNmzYUGititWtt94KwPHjxwsMYKoq95uQNgJ3FXJcWwBAW+SwIbAajgyBmtYmFm01ahMGpvO00xxSGEAgWh+aditeIGxoT9ux1EZ16Gf6how/k9nW7AmG3gxPxeSOls4xwtcHIP1HYNHfcg8kHYHko/D9u7AsC3SWP+AdX0DWDajVCNLcrUmxBjClMfxazX0vDy2Hg6u0WYZ1ekCXG9zYL5nwy3vUvHGcdd/9D4DAwEDatClODZMQojwxokeRYdTO9eyZe8PIzMwkO9txYjJPOv2YzWaHzrKAbcmCTZs2ceHCBe67r+j+BEuWLKFu3br071/0kNe9e/cCUL++m0NvhRPXtYceb0Jgg9yp+gEwwfdPArej1YH4AXcAr8PV9ZY0rnQ802oYms8Z7RC82LP2Tb85HGKaOx7r0hjubAkPLNaTY7SUr14zaB4NA17KDV4AtiyGzvfBmf0ulCuv0g5gLNfxD4KbXZhz5dJR/P74i1tuuaVkiyaEKFUm9OhKcRj1zJkz+fLLLzly5AgBAQF069aNN954g1atWhV63qpVq5gyZQonT56kRYsWvPHGG9xzzz0uX7dYjfM3btzg6aefpm7dugQGBlKjRg2HzVWTJk1i69atnDx5kgMHDjBp0iQ2b97MsGHDAC0I+eWXX0hMTGT58uUMGTKE8ePHO3wovXv35t1333XI12w2s2TJEkaMGIEhz7whiYmJTJ8+nd27d3Py5Em+/vprhg8fzu23305UVFRxPo5K7fDhw5jNPwJ6y6azPPrYbb5owYgfcL92You/QZth0G643TYKdAagJtpQYD0QCTwLqjVozZ1ErWBa02XQTQXN1ZJbc+SsZSjYHwZ0gD0v2QVXPgEQXAvC2+fuSz4OR7bCnU9YdpTnpQSKOV2y96ZYFkJUUVu2bGHs2LH88ssv/PDDD+Tk5NC3b1/S09MLPGfbtm0MHTqU0aNHs2fPHgYNGsSgQYM4ePCgy9ctVoj2wgsv8OOPP7Jw4UIee+wx/vWvf3Hu3DkWLVrErFmur1p84cIFhg8fTlJSEqGhoURFRbF+/Xruuktrgjh69CiTJk3iypUrRERE8PLLLzN+/HiHPKxNTPY2bNjA6dOnefzxx/Nd09fXlw0bNvDOO++Qnp5OeHg4DzzwAJMnTy7GJ1H5NWnShH379kHLB7Vv+IrOUqtimflVtTxX7eYuCWoEoQUHF/lv6NbAsSbgShNeBKBwYVUCtXp3dJ5Er8XmOYV0Q2lXHyb3g9fXAz5O+jdt+QhqNoIOfS075GYvhCj/TB7OxOtuDcy6dY5LvSxdupS6deuye/dubr/9dqfnzJs3j7vvvpsXXngBgOnTp/PDDz/w7rvvujypbLECmG+++YaPP/6YO+64g1GjRtGjRw+aN29OkyZN+PTTT201KEVZvHhxocdnzZpVZEBkncXXXt++fZ1Obw4QHh6ebxZeUbDg4GB04T0x9//M6XG92Ujcr/HEnEsgoWEM8V3jMOnc/bUyodXIRLiUOoAgDuJDo0XruLx+D6MPvEt2kNb05H8tjc8iRxN4LZ1rwCfZBedzPRN+tsxjx5mDNEq5wIvHtnOxU3/iB8ZheigeHrJ2/bWOa3KkV43EEU8MCSQQQzxxmBTr+y9eJ15fMlnL34jiIPtpTyxfkG3pG1Q4CbCEEN4LYFJTUx32uzqYJSUlBaDQaVW2b9/uMLIXoF+/fqxZs8blchYrgLly5YqtI21ISAhXrlwBICYmhjFjxhQnS1GOFdYAEvdrPNO2a5PD9Tm9AYDptxU1E3PeG+1RtFFLe4DH0Jqi7Jul/ND6xgQDeg7ybyLJRgHCTp7nvTrDuLlXFOgUjq/7jSCjCQVt6rhR38OSWmDQg97yRnQK6HQwdxMcsoweHtvkJmol/srkK2c4+OevcPYg02+11jop2lwwajYw1fKJaKOr4tjCNDahA/rwA/AT09U+luPHLe/vX5bn9u877yR4ZtvxtSzlTk6iAHfyE2u5ld6MIneGXvsJ8ayvDwFti/jchRDCdXmnCnnllVeYNm1aoeeYzWbGjRtH9+7dad++fYHpkpOT8y0LVK9ePZKTk10uX7ECmKZNm3LixAkaN25M69at+fzzz+natSvffPONw9BqUVkU/M0+5pzj5HAx5xKKkf9uQAf6COAvwGhpmrJsqsmyzwioNMLoMFldg8xsLq8/CpgJMZkcjgWZ4fFPiy7BfYm/0hf4FlgE3L5jFexYlT+h8qbdC5UYNcfWkUwHxLAR2ELuLDR6IAHY7tInARCV5/1FcRKYTuGhpA5yMly+hlUxJuIWQpRz3hqFdObMGYdBOa7UvowdO5aDBw+SkFCce4F7ihXAjBo1in379tGzZ08mTpzIgAEDePfdd8nJyeHtt9/2dhlFGVJV1XHobR4JDWPoc3pD7oRzDWOKyFEhdw0hq4OgbwiNE10q09kzkUQaT9oadc761IaeFwG4trkGNYzXbMfSgclok/pb6zisk9+9B/wN+EKBc3c0Rt18mgBVCze6P3cLzz7TBbNRawZa2nMF13URMOuA4/v/8jX6/Dd3eYKEB6bB/ZYaqBO/weQu0PRbCHVxAcjrG9l/vA93kttotZ/bgXWFn8ctWqWVO1Szy3MqCSEqDjMGjxZzNFvODQkJcWtU8dNPP823337L1q1badSoUaFpw8LCOH/+vMO+8+fPExYW5vL1ivUO7TvS9unThyNHjrB7925q167N8uXLi5OlKKdUVbWbuD+/+K5xAA59YIpmzPPaYDcKqWjtGxzg4F8daGQ8y1kU2huztJl8dTqaRJ/g1PZIqhuvcQ0YCXQh/y96MNAI2Gl5vap/U/6o4c/2L4/RoFYAO9+4gxqG3Ju7b6AP5OT/9hE/0PL+jyaQ0CrG9tqRe6OQYoG13EQUp9lPB2JZ49qJ7tamqHZDr4UQophUVeWf//wnq1evZvPmzURGFjaIQxMdHc3GjRsZN26cbd8PP/zg1mS4xQ/R7DRp0sQ2WmXx4sV88MEH3shWlAOq9SZ3/Sz4VIPsNKhWFwyWCed0hvx9XrJvwNXDUO9mSPsLzmyC46vhwm9gziH/6P2uYP4Isv8A3xZFlilDH0Sz8BOWFxsg+S44/gK0nEOaX3Vq3XEVMk7Az00pbMWs+4GvAFRY9+IW9nUJo92wtiTtTkY1OJZRVXF6szfpDUy/v6g+P+7JBnrzJtDDjbOcdzIunIqukNo1IUTFZPKwCcndUUhjx45lxYoVfPXVVwQHB9v6sYSGhhIQoA2wGD58OA0bNmTmzJkAPPvss/Ts2ZM5c+bQv39/Vq5cya5du9yKH7wSwIjKJykpiUmTJvHzzz9jPnMG/m3fmUuBkCaQc0MLSIIbQfvR0G40HP4Ytj4PpkxQDKCaAbP2PLht7vkOooHFkP4p+E5zr6ABfSCgL5x5D5q+AgZLdaelNqKw+oWawChgmg56Tu3GHa/EsOqhr6jRtLrzE9ytrbAFB8XpZ1IKfVNUcwks1imEKGsmdB4GMO59sVm4cCEAd9xxh8P+JUuW2NYiPH36tEOTdbdu3VixYgWTJ08mLi6OFi1asGbNmkI7/uYlAYxwatu2bSxbtkx7Uasn+NSCgMaACql7Ieu81qnWPxwu7YbN47QNIKQDNJ0AV7eDPhAaDIHAluBbA/4XmGd2XgDL+jumq8UrbM25cK4DHHgYOn1n2Vl0AGOlUxR8g33JuJrJ8fUnuOvNOwpI6ebN3rYStjvn2Y8wKmEFrCovhBDucGUwwObNm/PtGzJkCEOGDCn2dSWAEU7p9XbRe3B76PBuwYnN2XBtJ5z7DKp3hQYPgd4Pwoc7SWy/sKOX+LaFkKfg8vuQfhQCW+FKAGAd2KyqKpeOXGbpnZ9Ru3VNOo3qkD9xcfqLFCuAKa4LcOEqTK3uuNuhzHnKkZVCVv3CO9oJISoeo23mdE/OL//cCmDuv//+Qo9fu3bNk7KIcqRv3768+eabvPjiiyg3jhUeDuh8oWZ3bSsWa+4e9MeoPhWuL4H9QyB6vy2vwsqdibb0pGqGgyuPcNPw9vSecTt6n/x/vGazapvd12XWmia3Ap/ifgY3gAaQ3ZHcsVZ2syPb2L/+jpSrF4t5PSFEeWXCgOLRWkgVo27DrVIWNcdLaGgow4c7+9YtKppq1arxwgsv8Ouvv/LfralFn+CWvDdV60077+gkN+jrQPVX0F99kbiDjxGTcZIE4Fe7JApat9jGwGnL6/bAawYdvV64lfd+PEW9pu9z/qa6LP9uCGb/3D+PrJQcqB/oXpFMRuKAmL/iSEj/lfgw+1l6C2L9LIozi+8twCQ30ofSoMFvxbiOEKI8M6N3uyNu3vMrArcCmCVLlpRUOUSVZvk1VN2fiM1ByNPEpcxgWvJyy8y48Alw0nK4B9ra1wrQ1LJvK6CazcTN2k7EDW0CuYgfT9O/9YeMG9YWVdWamDKuZsGtN7lVnLitS5kG6DL20CdjLwDT6xcxYinzd7eu4cidmp5f0em+pnNn11d+FUKI8qRi1BOJMlMyM7XmzdMHaAw3/gvmD7R5/otDF0CMPgKdeZ/2Eq2G5aTlcGNyb/GK5TWAatbT5obj7LeNT6XyU/wvjvmf2udWcWJO77ObpVclJt2FmSmvfW55El5oMufc+Vl9itmczlNPydIfQlQ2Jg/7wHhSe1OaZBII4QIvdkJVFJw3j4wE8zVI92wixITA+225m4GvgXcs2+d2V34FaA5MA8BEHaC15ZgKrKMFWpNMMNo6TO5P1Z8QHmVXFoWEwKJmKQYUPdoqTs3dupb7pqLXt+Vvf3vYrbVHhBDln8nShOTJVhFIDYwoVFZWFuqlzXDsdW2Hzhd861hutDrLYoc6MOWA3l97rvMB9OBTAwIaWdICiq9lZE4OWn8X+18/S6NO5lbQBYMpCcyZYL6s5anU0PKxXTdEy0/RaddCDxiI970ZgoYTk/4tCeoV3vDVEdalHoqi8IHRhO+5dG65ns12s4qPSaVeu1qc3XURHwYzkyNc4jQ7aMJjXALqAnMs5RurLQ3w52/5a4jsJ4PT6WyzAse36g5bFhPj15qEoDuJD7kHbuy1JDTbjVICW+1JzgXgKrDD7lhB/WHsa1yyC0nnTC1MpndISXmERx55lLVr/0daWhqKoqAoCnq93q0pxIUQorQpqqzmViypqamEhoaSkpJSqf/Rt2jRguPHjxed0GV6ctdC8kGr4fAFLlC8jquFUYh9K4bbn++S78iGads5vCaRZ/Y+Spz+X6jmoWgrYQPsAl4GvgSsHXcfBc7ny6d8aQGsdPOcXwHnzUjLli2TTvlCeElp3DOs12iTsgl9SFCx8zGlpvF7aK9yf3+TGhhRqPbt23P8bCOo8TFaU1IOmFPJN1T3/M3g1w6aLEarXTCB+QaYrGktNQ6nHgOqAQOAa8AltNqDQCARrdfKW+QGNkHkLsuo5uZFBlogZLbbZ//8IPAy/tULXuHw0h/XiG/wIao5B/gJ6IdW65JjSeFjl7o7WkDzELmBDjjWglifW6f134PWWHUv0BHHHjgq+efEUfI8FiZvmqkU78+5K/A82md/E9ZJBXW6V0hKSipGfkKIsqYNg5Zh1KKKUxQFlAAwFNKpVLXUqPg2gsBbC8/wzN/BfB8wK28mwNNovVbigG8A/2KWGkAb8u/j5/xXPPzWMIYs7UvtVjWY33ElqGnAeOBDoI3l2v8GHreUzbL2EkGguLiytFoDLYDpihbAlKR6FL9L2yP59uh0r3tUGiGEKGkSwIhCaS2MRdQImCydQIuc4wRLXs6aihTgXSAKmAFEAE3QgggnM+O6SDU7byFtFZu7Wqqi6FDVXmhB0xa09aCnAPOBNZayWbv4lpep93OAeLRaLAWteauOV68gywwIUTHJKCQhsAQwrvaSqtbVhUQFBTDWY38H1gL/B1wG7gbOuVgAe1owZczOu+5SQXyBRsBfltc3Ax8Dq9DxOVOoQQ2gN3+iV3Mn3AtQ00hUI8lSfUhUIwlQ0+zy9HafHnvXgN1og8Szgcb4MpiN/J2L3MlG/o4vmSV4fSFEeWX2cARSRZnITgIYUSiXamBs39S9dcNuhzaj7Fdov6J3FyNvLb2uiOn/jdlGVLMZrT9NEtoa1fZCeZmvuZ2fuQYsYBNxxNuOHqQDkZzEFyORnOSg09qikqjJsEaVY4DVwGrWspY72UVtUrmTXazl2WLnbjbnkJkpAZAQovySJiThgqJuwJbjqrdrHCLRZnB5EhiJViPiKq0jrt7X+TeJ7yZspfWApqSdT0MLXn5EC5butKRYhzbVXXWy2MJDaD1k2gAx5E5I14izDl1vG3HWjTK6K81STsgdEZX7/qI45lCWKI4V+0pmcxbffPMNDRs2BOCWW24hKiqq2PkJIUqPET06WUpACFdYb5uutDW5UKPjIBYYDSy2PP8KrbnHNQVN6ptyNo2VQ9eSfsk6OV0SUAuts24n4CzwEXCdfxNAHPAclgnpyJ2Q7iyNiOSkbVzRWUpydefdaJ2MrXRoQ6c1+2nJneyylWU/LT241jh2717AE088AUB0dHe2bXNhJmEhRJkzoUf14PYuAYyoQixRgupKfxMd7i/a+Ara8OZZaB16t5O/qScvSzClcx4sDV2prQF07XQqbzT5CLgLbfK4qcBc4AnLBimYSGE2P7CRBAYRz91abZOioz0HOEgHGnGWszSiPQfcfG/usP5T+Raon+9oLPNYy7NEcYz9tCSWeR5c6xFUdajl+XSys2W2XiEqCi2AkRoYIXC5F69SUr/0CvAUWvAyErgH+KWwE3C1zIotwLkPeBXoDbwGvI1WI7MTE+uZzlZLOVZbNj2oDchgCc2UE05ydr8srtuFttyAHq2/kDbRVDb+9GaRF6+TW7OmKyAQFEKIsiIBjCiUa0NprX1fSvom1wOYjFZLsg9t4rWCaLVBekPhnXhPbbNO1mZEq9VYhNZk9Q+0PjAH0NZCqgbcQKuVqQX8idYfpQ+ovUDZ6CR3a0DnrQDGz/I4zW5fGPA/L+VfEJmsW4iKpKrUwMgoJOGCom5g1l8jF5qQPI5xBlmuN7eIdFqZdT6F/4rfuGwdaWOd56UH2mR61dD6wzyONqvvU5bjXdAmpnsYrYPxfcAmUG920onZ239endFmKZ4F3G/Zd2fByb1I5oQRouIwmfUebxWB1MCIQmkz8RaVyhrglEY8XAOtv8qmItJpwYTictOH/bIBrYFteY7bLxNgFQAMtzz+BxgM6oton4MOOJLnXE/pgZZAFtoik9XRuhaXNKmBEUKUPxLAiEIdOXIEMo7AGcvIH8UX0FtWYLas5WO2TN52/nW4shhbIKPoQfEBxU97RAXTdeAL4AB6zMRxmRgySMCfeEIwocNxnSUs19GhBSUK2uRtoDXhONZA+JLJWh4iir3sB2Lv+x/mAF9t0WwF0ClaCGKJQ7LSsgDQYySO+cSwkwRuIZ6n8qwHUlAzmQIMAc6g1dx87eRTjEena2N7P4qiopgymEQSMeSQgB/xBGOyTPKnJ4c4MizHfIkn0PK5WNd6umCX92By14GylifvmkoKWvPT/WhrOblfm6IoCkeOHGHNmjWcPn2GBQvmo9dXjG9pQlQ1JqMes7H4f5+qB+eWJglgRKEaNmyoBTFNBmo7zJlgNlqaS1TtUTXBjSTwq2npyGu5maomMGVb0mdr+zKsQYkPcVxgGlfQAX3IAPyYThi5N2H7G60ZbMFNMFqzzhbyBjBreYg7SUCxHFlrCKJ3+FBLOY255bUO5zZugfTTxLGCaXyCDpU+/AzAdJ6xy7mwWggFrSZkAFo/GWva88CH9OwZTf362qghRVFQFIWoNV/xwo10y3vPBkMg033qgWIgLieZaTmXco/5BDPdpz62PjXmutqjzt9yLR3c2Knt8+1qd327INB0CUyzgf1oyyQEFPJ+rDLQOkv/yI4dmbRp0wbrQpSvvjqNOnW8u3SBEMI7TEYDirH4t3fVg3NLU8UopSgzISEh0CgWeq3yTobLa0DOIFCWEKP2RccPgBaaxNABlO9dy0dtBaxC69Sb+2scxSHHydxUM3RbWHA+R/8N2/6PGA6gs/abQSWGnQWcUFDthQL55l25CHxIXFwcffv2dTiys0ZNdDfSLdeDmIB20GoDADFH+6LLOZd7zL89tCric/ktCIL+DrXmFJzm0ni4Ph+tA/JcnA3FhqtoK3P/iDZcPQfQg180VJ+o1bZdfLjwsgghRCmQTryiUIqilMDYIi1QSCAGsyX3vBPEFe1FIBnyBBr7aedQ/7A/tAWFsnROTaB9nrLcUtAJbpSxYHXvH2xr9DEDCenbbccSgvN8LsHufC6FqD0X6nwGnAaGAr9aDpxGm+V4FFr/oldBtw8C+kK97yHSCA1+gmr9QfFkhXAhRGkwGXWYjHoPtooRGkgNjHCBtztxavnFEwdoU/MnEGN77ZrH0NYB+hqItu2N5T92fWDSiL35bZdyi+choLZDH5jCyu6pJosWcQo4+tFHJADxvk1zy1Lf8rlcTyAhOMb22iuCHgTfmyCpG5jHAIFAOqADfV0IGAGhE8C3nfeuKYQoVSajHkX6wAjhZSpY+3KYFAPTmVq8fBRfUFujBTAzsFYmapO5fYXWDDKIImf9VbWAxIQ+T5+XvLw8143BgDplCv0++kh7nXnQdsikGJjeoDifi4vBlW8rCE+Ca69A1m7wuxlCxoGhbjGuKYQob4xGPUpO5Q9gKkY9kSgz5Xv+j3+gNSN96+SYZdK3nDQX8yrqfXovgDGbzeTk5ODv709SUhKvvfYaOkM1j/N1i84Xas6E+t9DzXg3ghcZUi2EKB+kBka4wIs3LQVygwFP83oK1OnAP9E60La2O2gZ9l1kAONqWayT9LkT8+f/3L799lsGDBiQb7/Ot6i1nYp3Pa9RzZD6Drrrs9H7+OLvL31hhCivVJMB1eTB7d2Tc0tRmdbALFy4kKioKEJCQggJCSE6Opq1a9fajicmJjJ48GDq1KlDSEgIDz74IOfPny80z4iICNtQVftt7NixtjSZmZmMHTuWWrVqERQUxAMPPFBkvlWV92tgvJ3fDrQg5BEg026/Oytk26cv6rj7QYL9Z3jy5MncAz4NodFbEPER5uab3c7XyZW8kEcBbnwFV57nb4N7snHjBoKDg0vuWkIIzxj1nm8VQJkGMI0aNWLWrFns3r2bXbt20atXLwYOHMihQ4dIT0+nb9++KIrCpk2b+Pnnn8nOzmbAgAGYzQV/a965cydJSUm27YcftGG6Q4YMsaUZP34833zzDatWrWLLli389ddf3H///QVlKdSS6cTrFUpjtOHUp4EFdgcswYxPETdal9+bd9Y1Uu2vV3s0nJ0AJx+Hah08yldTQgGMmgVpy2jVqh3/+c9KevToUTLXEUIIN5RpADNgwADuueceWrRoQcuWLZkxYwZBQUH88ssv/Pzzz5w8eZKlS5fSoUMHOnTowLJly9i1axebNhU8jXydOnUICwuzbd9++y3NmjWjZ8+eAKSkpLB48WLefvttevXqRZcuXViyZAnbtm3jl18KXuE4KyuL1NRUh60q0GoPynm/B+UetJFIs8mdvj9Qe8i6Uvi5qqt9W4pfA2MvMjIy98WFd7RHnzCP8tRYJuYrCVdeghtfYTRmcv/9D3DhwoWizxFClJ1SroHZunUrAwYMoEGDBiiKwpo1awpNv3nzZqctJcnJyW5dt9x04jWZTKxcuZL09HSio6PJyspCURT8/Pxsafz9/dHpdCQkJLiUZ3Z2NsuXL+fxxx+3VePv3r2bnJwc+vTpY0vXunVrGjduzPbt2wvKipkzZxIaGmrbwsPDi/lORclYj/br/C/La1d/tZ2tceSMd/5UEhMTbc+bRVhmsvUvR0OWVTMYz4Lxr9zaKXMqLVq0JjExkdWrvyQ8PJysrKyyLacQomAmBYwebCb3vgylp6dz00038a9//avoxHaOHj3q0GJSt657IyHLvKfOgQMHiI6OJjMzk6CgIFavXk3btm2pU6cOgYGBvPTSS8THx6OqKhMnTsRkMpGUlORS3mvWrOHatWuMHDnSti85ORlfX1+qV6/ukLZevXqFRn+TJk3iuedyF85LTU2tEkGMNpGd6l69g9kMulKOjZUgUFsCuyw7LNdXi1oh2zs1K4UZNmwYc+bMITk52aHvyN13382BAwfZunWjtkaU3pN+JQqkzgV9TQh+CvTVi5fN1UmQ8qb2vNYiCHkSFF/++OOILUl2djYpKSlu/7MRQlROsbGxxMbGun1e3bp1892L3VHmAUyrVq3Yu3cvKSkpfPHFF4wYMYItW7bQtm1bVq1axZgxY5g/fz46nY6hQ4fSuXNndC7eHBcvXkxsbCwNGjTwuJx+fn4OtUFVxcWLFzGf+xHWdCR3xI7Ospijqq1zhHXFagVMmZCaCDofCI4AQyAOtRw5qcDHoNYhd60es91zS/5YF4u0WzQy32tdnrSHLOc/iLYmEXBgJpz5BszZUKMD3PKm4xtU3A201gM1sa4JZHtfDo9WWj+cixcv8tZbc9m/fw8Gg4Fhw4YRFBREeHg4f/31l5b091tBVw3MGRDUHRq/71YQaPAPo12LIPbtexklZQZqw0TIWAvm62A6C4YIHD9Hy6bkWXMq82eaN29FcnIyaRnrQDGAIVyblRcF1EzI3OL6xyWEKH1GipwCq8jzIV9XCW/fBzt27EhWVhbt27dn2rRpdO/e3a3zyzyA8fX1pXnz5gB06dKFnTt3Mm/ePBYtWkTfvn1JTEzk0qVLGAwGqlevTlhYGE2bNi0iVzh16hQbNmzgyy+/dNgfFhZGdnY2165dc4j8zp8/T1iYN/oiVC7Xr1/XnmRkktuMYgLMWhODzj/3Oap2w6v3LJhSIf0XyLyinacoljRmwAd4P8+V7G/+qhvP7fdZV2z+BW12WeD6n5CWrJX53DpIPwt3rLDLxtVh1NZhzl+4mN7RLbd0Yf/+PRiNRj799FMUnS8KCmazZZFL4w1QsrQFJy99CFdWQIv1EOzaH7Tq24zU6yd49NFHWb58OZxtDGqOQxqdTg+oto7EagEdmLt0eYQ//zzBzp2r4cbqfMdr1qxNUFCQ629eCFG6vBTA5G1leOWVV5g2bZoHGWvq16/P+++/z80330xWVhb//ve/ueOOO9ixYwedO3d2OZ8yD2DyMpvN+drXa9euDcCmTZu4cOEC9913X5H5LFmyhLp169K/f3+H/V26dMHHx4eNGzfywAMPAFo73OnTp4mOjnaWVZXWrFkzfvujOuYWP3gnwz21gHvA9xPv5GdlPgtZ4cBEtBl4TwBDocF7UGuMFjwljYMT88G/FtxmGbGkWv/Ki6rtaGh5XAe42mflLNZlDnbutDRt1XofQv7uUN+UT8YmuPA3ONoT6j4DjYteDsHUaD6njnSmR4weHx8fqlevxX//+znp6emsWbOGOXPmEBgYWOD5zoKZggIca4c7IUTldubMGW1BXwtv1b60atWKVq1a2V5369aNxMRE5s6dyyefuH5vKNMAZtKkScTGxtK4cWOuX7/OihUr2Lx5M+vXrwe0IKRNmzbUqVOH7du38+yzzzJ+/HiHN967d28GDx7M008/bdtnNptZsmQJI0aMwGBwfIuhoaGMHj2a5557jpo1axISEsI///lPoqOjue2220rnjQvvM1lXnO5oebTUrCg+lkcF6s8F01X4/T0IaQVtn7bLoKgAxnozd6d3fu4f+/7EVhDYHgJ6FX1aQC8IPwEXR2sjlW78Bi03Fd6k5BuBuW4cH388mc8++4yYmBh8fX1RFIUePXoUGryA8/l+JEgRooLyUg2MdY620tC1a1eXB+hYlWkAc+HCBYYPH05SUhKhoaFERUWxfv167rrrLkCrGZk0aRJXrlwhIiKCl19+mfHjxzvkYW1isrdhwwZOnz7N448/7vS6c+fORafT8cADD5CVlUW/fv147733SuZNCie83GHWnAbGBUArICLPNexuwooOGv4bck7Dr+Ohehs3mpCKU2ZLsBP6ItR8w71TdaFQ9z9w+Rm4/h4cjIDIzwpsUtIn9sWUshWAcePGc/58bod0Hx9fjh07SkRERDHegxCiwjECOUWmKvz8UrZ3717q16/v1jllGsAsXry40OOzZs1i1qxZhaZxmNnUom/fvgVWf4M2HPtf//qX20O+RDljNoNpChjfAbKA59BjJI5lxLBNW+VZVXAYh6TzhcZfQmJX+OFeaPWk5YD3axt8yWAtEJUyn/1ZO4mt9x3ZOjem4Ff0UOtdbbHFy//UmpTC50K9f+ZLqjNd4N6BAxk1ahTr1q3jww9XYlKXAgfIyZnC+fPnJYARoqowAUUNwCzqfDekpaVx/Phx2+sTJ06wd+9eatasSePGjZk0aRLnzp3j448/BuCdd94hMjKSdu3akZmZyb///W82bdrE999/79Z1y808MKIq8UINjDkRsmuDMR5oAXwE3EQcy5jGv+nLIaYBcdedLPRoqAmR34OuBvz+rmVnUQGM+2Vey9PcCdQmkzszf2Tt+XvczgNFgeBR0Pgv8OsMZ56Bw120UVV5NGvWjIEDB3L58mVMpmughIEySCu912dTFkIIza5du+jUqROdOnUC4LnnnqNTp05MnToVgKSkJE6fPm1Ln52dzfPPP0+HDh3o2bMn+/btY8OGDfTu3dut65a7TryifCm3N76snmh1pPOArlgDkBj2obMEGzogJvsP5+f7NoXmu+D4bWA8A7wKvAnULuCC9kO8XRPFMYcB1lFZ+1w+Nx9dCNRPgJS34OrLsLc2tN0N/i0AMGVdYuXKVfzyyy7OnDlpOakjoH0rKrc/RyGE93mpD4yr7rjjjkL/xyxdutTh9YsvvsiLL75YjII5khoYUcq80FRjvgEkA48CtzrkmcBNmC2vzUCCX5uC8/FpAOHLLC+2Ar2A/+K8tsW6z9flYu6nlcNg7/1+N7l8rlOKL1SPg3rfav1+DneC81rnZbPxBn/9FcS27eGcOdsDdO+A4keJLvAohCifjF7YKgAJYEQpK3QAsWvM69EaafMPZ45nBNN4gu9pyzQgPmRwEZlpN/iQf89A16Q6MAF4DDiTJ521R5yPy8WMZR4/Apfw50f/O4mt953L5xaqWn+o/xPo6sCZp+DonWhz8AwA/XJt0z3rcIrUwAghKhtpQhIVj/lrtMCjfb5DJgxMZzRazcwTlon2imaIbES9k1u4/tp80l57D0x3oQUzI9CCFuvcRK5P4JaNgd4A1SdBjakun+cS/+7Q6A+4MhFS51jKuAbM4YABlHag9MAaoH399dccPnwYnU5Hv379aNiwYcF5CyEqtlJuQiorEsCIMuBpDcwuoBEQUFgiy6OLlYyWGorgqc8Q8ORQrtw9EtO+GcBioA9gnT7/XcAy+7BDv5hqQH2gNdpwbh25Xfldr7Vxi2KAmrMsAQzAMTBrI5QMhiYYOQnUQacL5Y03codxjx8/nrffLnpyPCFEBWXCsyDEkxFMpUgCGFHK7NcQKib1BHBnUYksl3Nx4jm7SdsMYXWou/d/ZHz+P65PnYv5xJeo2Vlo87ostls/yW4hSNX+P0Z1IBaIcu3aHrGf76YTBkMyRmMSimLUDim1MSvXbHGcj64j2dn5RzAJIURFIwGMyOf06dP8+eefmM1mzp8/j/n6Hkh+2xIMWDadHu2uqLfc0PVoiwNaFno0G9Fu7DmASbvBqyYwpQCbIOdtcr8mWCctyNLywNeyGXKfK/6Av+V+nY6z5iNH1gCm+LUfAQ/2J+BBbSmKa0/GkfHhSrgvHfQFTKd94yyc+xzO/QeurQb1M0Cnre7s3xUC3BsiWCA1S+vIrPiCUg3Q4evrR3ZOL4zqC+j0j2FWrzumRwtaVFXFbHZ18j4hRIUkTUiiqurdux/Hjx9x3Hn2eS/lrgAXwehpfulFHHe1CUmrRVFNhdeZKkHVtCemjIIDmGqNoMVz2mY2w9EZcGQqqNcg+S4IfBjqrnB+rqvMmfhfu5vMlC1aQNngIASNguz/WApaDzPfa01iCqBeRDE3RlW1lbGNJvD1Lar2SghRoUkAI6qqtLR08B8LAc+Ru8KzZSVp1WR5bn1U7Y5bNlW1NMno7DZLjc21LmgTzy3Oc1yH9utozdu6WWpwyCb3r3IYsADYB0zHfs2hXC7O22JpOvLNzOLzXo/Qdt8RDt/Umke/+4hsf60DsN5oZOIve7gVSPjjTeLbvIZJV8Sfjk4HdftqAQzzgV8gfQWcvwH11hR+biH05hReSNlC/5AQ/peaSvzVVzGZrCOm7JdNsD6/jKpmMm3aNNq00YaU9+zZs9jXF0KI8kICGMH169dZuXIlo0aNyl38UqkJ+qbev5gSAGokEOlBJgeBl4BPgDXAQ8XPyrIW0udT5tJ93+8oQPcff2H5PY/z4CattuSZ+Pd4fvsedECfY7NA78/01q6MKrLWAgUAkwF/uLEErn8MwcOLVdy41PlMA3SpqdwCcGMl0wGfwBpk5xQ850vv3r2JiYkp1jWFEBVMDp6theTJuaVIAhjB5s2befLJJ/nvf7+kfft2JCefgYCSmiJIxfmKzqno9b1QlBSsNTKKosdsDsJkWgOE2aX1BeYC3wE/U2gAc/IeJ/1gFG1TAHMG4EP7P087zJrbdl9uE1rXhF22ehwdKjGXXFwx1ZhmeWK9/rNaeS89Dtc/tNSS2HdoVsgNeix9imy0dDFZ++3KAjFA+/YduHo1jRs3PkPHTsuAKu09quoNby+dKYQo70p5LaSyIgGMQKfTbonr169jw8YdWvORv/OVvEvOZUymEzzxxBNERkZiNpv566+/WLhwIbAJuAuok+echuSfcC6v7qB2xBa0YMbWLKUC/AAc52DzFvTYc9g2RurwTa1tOfwaczM9fkiwNKYpJNR2sSZDtX6NsYYcPsAsYAhkJYBfE2xBiqqCORP0Ado+2yrZKrZgC0jQqfQx5TbsJaDj0JEz1Krhx5AhPTEYDKiqatsAqlVrQ4cOHVwrsxCi4pNh1KLKqf47Jn0TrZmnxNjXMuT3yCOPcOedWifTEydOWAKYf6LXt8dk2pIndQ3gzyKuNxltiYCCfA/046HZL7NqxgKHPjBW8+OeInPdFm7dvoeElpOIbxlXxDWtrDVN9nUgrdBqYuZAg5cgbIy2+8hguLpG+6fTMA4az3CaY7xqhLPxxFxPICE4hvhGcaiZJ7hysCMdOnRg8uTJLpZNCCEqNglgBIq1w2fGm5Zhuarj5nuvtnlFQU1I+UVGRnLixAmmT5/OJ59sIv9AoSAgA1sthQey/X1tfV7yMhkMzLqtEze274GWL0JRHXitbOnyNuKMBH6AE8+AKQ0ur4D0vbmHz8Vr+yPecZifBsCkGJgenqf/TUAL9P5hpKcXNTLLdWvWrOHPP/9EURQUReH222+nc+fOXstfCFGCZBSSqCratGlD585duZGx23bD0lkeT5/+k9TMRFSvBjCu74+IiKBu3boFnNMc7S/tIlBQmqJYm3CKmBtFsWvqcTlra6CWN/IyAi8Dj8BpbUXWRo0aMWvWLJo2bUq3bt0geT5kn4FGUyGwo+vX9JIhQx7EZFJQdD6gGvHx0fHDD+vp0aNHqZdFCOEmCWBEVREZGcnu3TucHnv44YdZteaylzuCFtxBWFEKqklxtt+6mON58gcwLg6jtk14V0S6Yi2G6CzP/WjDwHODmuHDh7Ns2TLba7PZzMyZM1n0wWLOHOyK2vw/UKuoRSm9y2QyohreRzU8CWomOcZ7iI29l61bf5SaGCFEuSCrUYsy4K1w6LjlsWYhaVwLYBSDi38KBQZYztJa87R/v5cAE3Xq5HZI/vjjj0lKSrK7hEJcXBzHjh6mZcsWcPVr16/poZ9++glFUSwdgC3lV/wxG74iM7s1vfv04/fffy+18gghisHoha0CkACmklFVFaPR8bfv+++/d7hBlj1vBTBfAfWABp6XRef9GhhfNYeNwEWeZSMj8SUTbQg4XLx4kdatc0c6PfroY/nO9/Pzo3r1Gth/XnrVyJQzr7H+cF+mnHkNverd/zTh4eG5L/R2zYZKMCb9Wq6n1aN3736ynpIQ5Zl1FFJxtwoyCkkCmErmjTfewMfHh1atWhMfH09wcDD9+vWjQYMG3HVXPy5fvlzWRcTTDre5TgI3e5ifi01INq5fa+2BCdwJ1CadO/mVtfwDuBWIBuDIkSPcd999AGzatJEZM2bk64ibt0kt7mw8085Oo2/KD0w7O424s/Ful6swERERLFiwAEXxId8Mx0pNTMo0kpLOcPXqVa9cTwghiksCmErm3LlzACSeyODll18mLS0NdM3A7yU2bPiejz76qIgcSoM3brbPAZnAHUWkK2rhQmsNjKtlcr0mJir9uMPkeFEcRZsLZjHW1bS//jq3eWjy5Mm8+OKL5OTk8Pvvv9vVcuReM+Z6AjrLax0qMdddnFTPDYMHD0ZVc8C0zslRbXkFtVh9goQQpaKKNCFJJ95KxmAw4OPflhz/vaCeB3MSGG4BQGdcxIsvvsiMGW/kO6+gG1JaWipmM5DlS+5kcOAYhNjvU3KfqpB787UOyzbifBh1wTfEq1evYjSet7y6BLwBaJ1efXwWAR/kOV/FbE7HZAK9fgQ6XbU8ZbDSkZNjmXFXX/jQbnNqmvam1kVY+sHYvX99ADR7Vnt+YiEYUwHYb0zjTnI/iv20KiD3J4B/AwrvvbeQ995bCMCIEcO1GpiLH8OVbwCVBHMGfbCbyC51K+wIJcecwcKFi9iy5WdbrY02ogyCggKZPfsNTp8+zdSp0wgODkFRFPR6HSNHDmfYsGEOpWnYsCEGgw9Grjgpq2XxSwlghCi/cnB1toqCz68AJICpZGxNDooPKI1A18h2zOz/MZgPkeLQfaGImgdzPNoMuI+j3TKtizjaLfDosFkWY7R1ArUs4mh7/gbO/7K0mhKdk74oa9euQ2sq+h863d/R6Uy0bt2BmJju6PX6PDdsbcvJyeHAgQN07twZvSU4sT9unan2+PFWfPXVV6gpqRiPJIKfL/qIRvmabhSDXntvxpaW8tsFZjkn4dCLltd1gWYAxHKRtZwgCj/2cxOxvG+X42C02ozhwE1oE9y9gNZsk6V9ImYzT4/9B9u3/QSmq6AfQbyiB/YRoyaToIQRr3TWAirdKVLS6rH917wBI+j4iV133sXDD/+N3bt3gnIb6BqDepENGx5l//4DzJwZb/vss7OzMRpzwCfQyc9JAhchRPkgAUxV4jsAGODeOZkLQG0GuDr7bFHexflNUOs1ps9TE3L+/HnOnDmDqj6LwTCT22/vxn/+s5LatWt7pTQbN27kq6++4krPobZ91f+zgIAH+zukU/ys/UH+D21xRntmtCUNFKAR1pbZbC7Tm2eBMcCTec7pY9msYtFqYc4CWcyYMYPx48cTEBDAsGHD0Bm6YvZZiglt/W13mNULXE25nY8/Xq7tUH8Bw8egNAfjXN58cwJHjhzl008/ISgoiBs3bljetLMARgs08/6chBDliKyFJCqu8vwtWcX5r53zG+PcuXNRFF9UtTVG4++MGfOa14IXgF69evHbb7+RkZGByWTi9ttvR72Wmj+hbWSXs25jOqBJIVdx5b+BDhgP/AOA1q1bExCgBUrDhw/n08+KWjKhEEpdTPoNpN+IAa6B7iZQIrSaG5/nQNeS//1vKNHRPRg//p9cuWJtOnK2pETBNWVCiHJCJrITwsqbAVFB0/7nr4E5evQob701B7N5LLAKgP79+zs5t/gURaFTp04Or53N9aIarUFISd64e6AFQqc5cOAA999/PwA+Pj6YjL+C+WHQ9QVDMRbaVBph0m8FJQH0Qx3fo/5eTMrPHDl6P6NHjwbA4NMBo+5OJxlpAUzBEw4KIcpcFVnMUb5GVULG7LOQPhpMJ72UY1EjedxVdACjqipjxz6DojQCXgT01KkTZquVKCl5O6eaTp8je/tvmI6fsuwpzo3bnSHaDwAKS5bkzsw7btw4Bt53D/Xr/oRBebcY17cWozEYHnE+GZ8uCqP+D/A3gb8Jo36fNCEJIco1CWAqmX79+hHTvQtkf4QucwSomR7mqKDHzBReYz19mcJr6D0K7a2de48AO+y2A0DujfGrr75i48bvMRrvBrYBu7ly5WKpjH4xJV/EeOxPjImnuHrr/Vzu9jeyf9xe4tfV3A2YOXXqBJcuXcJsNtO+fXvWrFnNffcNwHtz6DihKNp8OIqukBmHtc/faDSSlZVFVlYWmZmZZGRkkJGRwcmTJzHlX3VTCFGaZBi1qIj69etHv3792LJlC3373k1OagNUv+lgPoxiPobq8xD4jnZjSnyFOE4yjWnoUOnDBgCmM7WI8wqTBHR3eqRGjRoArFtnnYPkfcsGJhNkZGRQrVo1p+d6Q0C1aqRNnUva1Lm2fVOnTsXX15fJkycXM1d3buiNgDbAEerUqUPt2rVJTk5Gr9dbgrcybroxait22y+FkJeffwDDHnmEfv360qtXL6/2WRJCuCAHz6onZBi1KEs9e/bk889XMmjQIMh4mhYt2vLHH4chZwM683rMAatcziuGa46Tp+HJ5GkqkAHA8uXLbQsDKopCcHAwDRs2BODdd99lypQpqKqK2WzGbDYTHBxcosELwMEDB/jrr78wmUy2moRu3brxn//8x5LCnQDC2gTj7uzH96LVUHXm0qXfMBgMrF27tnwEMEoYYICH/+04H471+dVTZGWn8fF3q/noo8UABIeEsnHDD9xyyy1lVWohRCUkAUwldt9993H48GEaN25MYGAgRqORRx99lC+/OuJWr5YEQuljCWLMKCQQ42HJtGCoadOmtGnTxmkKg8FgC2ZKU9OmTWnatGm+/cVrurL2E3G3SeVuYDbou4DyNzDGERsbyxNPPEGZBzAYwTcQbhlReKp74uHyn/DHJq6v+j/WrVsnAYwQpUWGUYuKTlEUhwDBYDAQGBhITuY+fMwNMOkHYvZfWFQuxNMYGEUMCSQQQ7xLc8JkoCiRqOp5fHzCbHtzcrKAzUDFHIqr18ejBSYKJlMttHlhCu8v4n5dbn2gI5jWQMAFMGqfd2JiYiHXKi05rjc/BtSAgFBAa4a79957HUZ8CSFKSBUZhSQBTBUzbtw4GjduzHfffcee/duLrolRFExqcfq8+KOqFwCYMuUp295169bRtGlTmjRpwk033eRmnmXnzjvvZOTIkeTk5KCqKitWrLAcGYw2U7G33QvMAPNR254ff/wRneFuz6YI91hm0QtfntiGbuPrqEe/RzVr/wkfffRRoqKiSqF8QojStnXrVmbPns3u3btJSkpi9erVWveFQmzevJnnnnuOQ4cOER4ezuTJkxk5cqRb15UAporp0KEDHTp04NKlS+zZvxVUS12j4lvAGTqch+Nm4CjwI/ArcBi4CtREm3V2NHp9DI88EsGUKVNsZ9k/r0iaNGnCkiVLAK056ZtvvuP69TCglgtnF6f5qR8wA4zTwe8EcBGoiVlpUIy8vERNAfMGqNnY+XFjFnz7Ivw0n9bto3j63QXce++9hIeHl245hajqjHjWidfN2pv09HRuuukmHn/8cdv8VYU5ceIE/fv35x//+AeffvopGzdu5IknnqB+/fr069fP5etKAFNFHThwAFQzfqa/k3V9MQRtBZ8e2kHjz5AxFTCD+QRwAm3UUBZakHIJSMcxsKmDFrwcRVuccAJmsz+KEllq76m0XLhwgevXr6GNGPrRstfZIpfWIex7gC8sz63BjAG4DgRbXpvtHo3Y1jIyrbSsK2WyO6462ZxR0P6L2S8+qcvdr+TdFwK6Tnbn2j+mgfE9LYgZvCb/pS6fQL/8QZSk/cyZN4+nn366QjYRClEp5OBZa7Obo5BiY2OJjY11Of37779PZGQkc+bMAaBNmzYkJCQwd+7cihPALFy4kIULF3Ly5EkA2rVrx9SpU20fRGJiIhMmTCAhIYGsrCzuvvtuFixYQL169QrN99y5c7z00kusXbuWGzdu0Lx5c5YsWcLNN98MwMiRI1m2bJnDOf369bMbulu5nT59mi1btoCuPopqWeU57XYIPQdKTbhu30k3EPBFC0x8gVCgK9rNO9yyNUBbhBC0G+yr+PjspVWrSPr0sV/vp3IwmUwEBYWQlnYQne53rAGEdYHI/I6BW01w9v95VDCvwPFPVcExKLE/xzpSyX5RR/uGwrxBT57yFtj2rUBATRjyGfx/e3ceF2W5N378MzPI4sLiEQEFEU058OSCO4tlKphlaZunTipW6pP9PC7UUdGe3JLUssUWzcytxcw8ejyVB0nEBBU1tVxxNzXQcgHcEGau3x8DIwMMATMsA9/363W/dO7luq/7Yrm/XGurIp24D25AtzqaZp4erNuxnc6dO1t8MiGE/cjKMl9WxcnJCSfTunAVt2PHjmLvhn79+jF+/PhypVOtAYyvry9z5syhTZs2KKVYsWIFAwcOZN++fbRs2ZKoqCg6dOhAYmIiYGx+eOSRR9i5c6fFv+6uXr1KeHg4DzzwABs3bsTT05Pjx4+b5hcp8OCDD5qaBACbfFHsxciRxvV2cPkI5TgI7uS//DKbg+7eImffAGYD/1PG1LVAAI0bn+XAgb0Vz2ReHsTFQXIyRETAlCngUDMqDJs1a0Z2duafnlcQ0BReLbtASLtOPHJQEUHj/I7RsehxALToMDCFOCJIJJkfieOf6JkBgI48pjCPCLaTTBhxTMy/rvRjf+5bYDAZGRkcPXqUXr16wZBV8D+PotPnMWXr20Sc3U7yxcPE6fPQ6xxAnwvfxcLW+Tw8cBArli/D3d297AUphKgcNhqFVLT5d9q0aUyfPt2KhI0yMjKKVUR4eXmRlZXFrVu3yjzjerW+ER55xHxl5NmzZ7Nw4UJ27tzJhQsXOHPmDPv27cPV1RWAFStW4OHhQWJiosW/7OfOnYufn59ZcBIQULwZw8nJCW9v72L7LSmYdbRA0cjUHiilGDhwIJs2bQSnGNA2hTvfmo63bdsWT083LlxomV8rVh9js5Cl/jGWWb1WTlwcTJ9ubD75wTh5Hq9ZM3le1dNoNBbL4YWLv/ESF9ECfdkC6JilMT7fFPV6oYkDAT5kFlMBR6Ywj+nMzj9mbL6alT8qrLRjf87Y6O3o6EhYWJhxl/4OONZnyqaZTE+YaUz3WP5Ehl2fQ/vF39Cc281b77zDuHHjZH0kIWoKG/WBOXfunOn9CzXvD/0a00it1+v56quvuHHjBqGhoeTk5KDRaMwKzNnZGa1WS3Ky5YnUNmzYQJcuXXjqqado2rQpISEhfPLJJ8XOS0pKomnTpgQGBjJ69GguXy59srE33ngDNzc302aPHROzs7P5z3/+Y/yQ8zZkh8ONu0HksWPHSElJ4cyZM2i1bsB6YBnQppx3Uta/zJKT8/t+YPy3lK+5Pep4/brph6/o5IARJBeaOBAiuAn8K//Y9iKTCm4vdJ3lY3/O+BvLwcGBvIKVtzXG4U4Rp5PN0z24Ht27IXjdOU/ytm2MHz9eghchapKCYdQV3fJrYFxdXc02WwUw3t7eXLx40WzfxYsXcXV1Ldd6d9UewBw4cICGDRvi5OTEiy++yLp16wgODqZHjx40aNCASZMmcfPmTW7cuMErr7yCXq8nPT3dYnqnTp1i4cKFtGnThvj4eEaPHs3YsWPN+rw8+OCDrFy5ks2bNzN37ly2bt1K//79S13DJTY2lszMTNN27tw5m5ZDVXB1deX8+fMcPHiQgwcPcujQIQ4fPsz9998PtAS+BFYBqzAYPsOaXmBWv9AiIu52MtVojJ9rkd1OzoW67ZpPDphMBIb8sjceqw+8CyiSCStyLKzQdZaP/bm7AYzp50BrrKBNDiicH0i+sI++Pbtz4Od99OjRozyPLYQQhIaGsnnzZrN9CQkJhIaGliudau9UEBgYyP79+8nMzOSbb74hOjqarVu3EhwczJo1axg9ejQLFixAq9XyzDPP0KlTp1JHNxgMBrp06UJcXBwAISEhHDx4kEWLFhEdbZw99Omnnzad365dO9q3b0/r1q1JSkqiT58+JaZrq85L1a158+bFZrh1dHTC2Bm3MuYzqaAp+U0fhfvA1CILGrpy9VoQEbgUmxyw4P93Jw7UAzOB7cQxMf/Y3X4ud6+zfOzP3Q1gTE2lWmMNTFzoaPg9jZ6HNpB85wYNZs3i+9hYGWUkRE1l7VpG5bz++vXrnDhxwvT59OnT7N+/n8aNG9OiRQtiY2O5cOECK1euBODFF1/kgw8+YOLEiTz//PMkJiby9ddf891335XrvtUewDg6OnLPPfcA0LlzZ3bv3s17773Hxx9/TFRUFCdPnuSPP/7AwcEBd3d3vL29S5zqvYCPjw/BwcFm+4KCgli7dq3Fa1q1akWTJk04ceKExQDG3uXm5tK7dyRHjhxBpzN+2QtGzPz+ezpwvw3vZuC3334lLy8Ph4p2vHVwsLs+L+WRo9fzuuYcGm0AqETQJKJDoVQ2BpyZpT6H/D4xqDzgLeA99HxtsV+LHody9HkpyhjA6HS6uzUwP76DLmkO+l9/YhYQGh7B22+9KbUuQtR0eqxrXylnB+A9e/bwwAMPmD7HxMQAEB0dzfLly0lPT+fXX381HQ8ICOC7775jwoQJvPfee/j6+rJkyZJyDaGGGhDAFGUwGMw6ywKm1WwTExO5dOkSjz76qMXrw8PDSUtLM9t37Ngx/P39LV5z/vx5Ll++jI+PjxU5r7kOHTrEY489yfHjR4EewD0Un+fDli8l40/OnTt3Kh7A1HITJ04gNTXVbIRSwf+/+OILdLr70OtTQOMPGgdQj2Fs4jsFWA7gKy4PrVaLVqu92/x3ZjuPPfkUA2b+g6CgILp27Sp9XYQQxfTq1avU9eKWL19e4jX79u2z6r7V+naJjY2lf//+tGjRguzsbL788kuSkpKIj48HYNmyZQQFBeHp6cmOHTsYN24cEyZMIDAw0JRGnz59eOyxxxgzZgwAEyZMICwsjLi4OAYPHsyuXbtYvHgxixcvBoxVXTNmzOCJJ57A29ubkydPMnHiRO65555yR3/2QCnFkCHRnDp1G1iIMXipbL4AdO3agy1bfqBp06ZVcE/7Utp8B3PmzCE8/H5+++0B8vK2gsYPWACsBj7CWBtja3mmmrnGjRvz/fffExISUq6RekKIGiIP6yays2YdpSpUrQHMpUuXGDZsGOnp6bi5udG+fXvi4+OJjIwEIC0tjdjYWK5cuULLli2ZOnUqEyZMMEujoImpQNeuXVm3bh2xsbHMnDmTgIAA3n33XZ599lnAWEX+yy+/sGLFCq5du0azZs2Iiopi1qxZtaKPS1Hfffcd+/f/BMyjaoIXgK5AOIcPp5jG+l+4cIFmzapxGnw74uvry7ZtWwgPv5+MjAfIy0sCjS+oHsBS4FXA3cZ31ZsCGKBcs2oKIWqYOhLAaFRp9T7CoqysLNzc3MjMzDQbJ1/TPPHEk6xffxCD4X2qdiVjPbAV2AMkkJSUlD/aSZTVmTNnCA+/n4uXnNDnJQGXgE4YJxYcb+O7zaRBg4+4fv3PJ+gTQpRfVbwzCu5B30yoZ8U9crPgh5r/fpNhBLVYbm4u27alYDAEUbXBCxiXTO4NGBf2cnQs/2R4dV3Lli3Ztm0Lnk1uoXPoDXhj7P/yPtYPMyhwDfh/wBz69o20UZpCiGqVh/FXREU3O6mBkR6WdshgMHD48OG7E46BWQeqli1b4uHhQUJCAr//ngF0BM4C1wvOzv+36Ho6CrgFnMf4oiw4z5/yNVkojAs+KsDY81w681ZMq1atTM1Jly/3Rq//J/AisA4YbGXqieh0I3ByusH8+QsZNWqU9RkWQlQ/Pdb9zWrNMgRVSN4qdmjlypU899xzFo+Hh/ckOflHrl69mr9nupV3DIP8tXjK5ktgudkeX19fK/NQd91zzz1s27aFzp27cv16KtAEmA88hTW/pRwcphIQ4Epi4m75+ggh7I4EMHYoKysLrdYJg2FVkSMa4DV++y2d1atX06pVK9asWcOJEyeIjY3FWBPzYpFrii6a8RLjxo3jpZdeQqPR8I9//INNmy5Tvp5S1/D19efTT40jvzw8PGrtEPWq0rZtW0JDQ0lIyMTY/+VVYBtwX4XT1Gju8NBD/SV4EaK2sbYJSJqQROXSAkVXjgZowOnTB8xmG74rHGO/FEuuANCzZ0/atm1rTK1BA5S6Wso1JVE0bOhKVFRUOa8TpTEYDBj7FsUCb2BcXqDiAQwY0Ol0NsiZEKJGkQBGVIW4uDgWL15m6sOilLI4IVDBJGLZ2ZlYbjo4nf/vG0BrjJ00PQAvjCtLl+YUgCl4gYKXZnmbKQzodNI/3Nby8vKn19RoQT0NfAqcxPh1rgiDLAcgRG2Ux90ujBUhfWBEWSQmbuHsWT1QsIRB4WChtMDB0mysbwPZlF7TYolxFdCUlBTatWsHVDSAUeTl5ZGRkQEYa3EaNWpUgfyIwu7WwADMAVZgnJywohPbSQAjhLBfEsBUM2Nty1+hXAvvlaarFdf+DzCYmJh/8uCDD9KyZcv8l2Z5X3L1SEs7ZNbv5bPPPmPIkCFW5E3o9YW+FpomoLpjnNhuKsZatnKnKAGMELWRtTUoUgMjysJgANvM0WIAvsU4VDoXGFbBdCeTm5vM0KHD2bRpIwaDwtgstZC7w661+f82xDgSpmg/iiFAZ4x1mDfRaN5m6NChLF68hIiIMMaMGSOz8laAcZHFwgHHR0AIxqakVyqQovSBEaJWkiYkURWMNTC2CGCOYv4SuxdjEFFeDcnLm8P27SPp0SOCRx99iBMnTqPUEQwGhcGgx2BQ3Lp1g0uX0oFQjPPEFOYKdDd9Uup+IJXt29eybdsbBAQEMHLkyArkrW4rVhumaQ+qDcaJ7cYC5Z0s0CCLMwoh7JYEMNWsYk00JXFDo2mAUjfyPydQsQAGoAcGw2oOHRrD2bMLSUjYSNeu5k1TycnJ9OzZs4zp6YAw9PowQGZ7rShjJ96iNSZvYJzteB3wt3KmKDUwQtRKdaQGRhrAq5lSFekkW1QiDg7/i1YbhE7XBgCt9nMgzYo0g9Hr13L9umOJS6HfHSm1HeOkdf/h7vpH/8XYrLEWOIxxFuD1aLWT8/Mm33YVYd6JN5/mMYwT271LeX9jKSWdeIWolWQpAVEVjH1MjgIfAn+nbJ0x0zEGBwVh8iHy8o7l/9+B559/nu3bUzl+/BX0+rWUv2mhQDZwG2dn52JHmjZtilarxWBYWmjvArNznJxcyMm5BYBO50BkZCTBwTGy0nEFWa6tG4NxtuWdGJv0ypyi1MAIIeyWBDDVLCIijLS05Vy+/B7gBzxahqu+RaN5Hy+v5mRknAfA3f0vDBjQn1u3bjFkyBDGjh1Lly7dMP5lXpERTufQ6Z7Fz+8vjB8/vtjRwMBAMjIyWLNmDY6Ojty+fZupU1+jf/9+jB8/jjZt2uDq6srBgwe5efMmrVu3xtvbuwL5EAWKd+ItMAJ4HePyAt+UI0WpgRGiVtJjXROSwVYZqVwSwFSzuXPnMH36NOrXr0/Zv+PycHdvQnr6OSZOnMSGDd8xaNAjzJnzhtlZcXGvM3HiJCAIGED5mqricXDIYseO/RYDD09PT1566SXT5xEjRuDk5GTWMTQkJKQc9xSlMRtGbaagzvd74DjQpowpSgAjRK1UdIWY8pIARpSXTjcHrbagGaZoMHN35Wi93rgWEsC8eXOZN29uienFxMQwceJE4GXgR+DNcuTGwJ07t2nfvhPz589l6NChf3pFSU1NwnaUMqDRrEOrPViwBwCD4Wz+WlXGztI6XQez46DQaFShfcb/5+VdkwBGCGG3JICpAVxcXPjggw84f/68qfZCo9Gg0WhMSwsUXmJAKUWHDh1KSxIAnU7HypUrGTZsGPBvNJqbKDUc6MKf18Y8glJ6Ll9ezs8//1ymAEZUrpdfHsvWrVsBzGq5DIZmrFp1BtDz978PRKfTmb5/in4/Fb7WwaETAwcOrNJnEEJUgTpSA6NRlhbeEaXKysrCzc2NzMxMXF1dqzs7pcrJyWHFihW89da7HD9+BJ0uGL1+GMZmJUsdfI3z09SrF8X48U8xb968qsuwEELUMlXxzii4B40zQWvFPQxZcKXmv9+k/rgOcHJyYtSoUaSlHSI+Pp4+fVoAk9HpemEc7lw0hk1Bo+mIVtuR3NwzMtmZEELYEwPGjrwV3eykBkYCmDpEo9EQFRVFfPxGjh49ytChA4C5GOdyKWw7St3igQdCee6553j66aerIbdCCCGEZdIHxg7s2bOHVatWFesPU1LfmPIcv3nzZv4dLhe542EAli9fjq+vbxU9pRBCCJvIw7r5Ue2kY4kEMHZg0aJFLF36OQ4OfhQejWSkKWEz7lfK/HPx4+DgMIS8vAcLpXcHne4IY8aMk+BFCCHskQQwoqZQSqHTBZGb+3UV3C0Dvf4KDz/8cBXcSwghhKgYCWDsgHGujqoKiWVqeSGEsGu5SA2MqBk0Gg16/XF0ume4OylZ4e8wVcIGxsFD5vuK/7/oOXnk5hZMWy+EEMLu6JEARtQMQ4cOJScnx2xysqITlNlyn4uLC6Gh5VkUUAghhKhaEsDYgZ49e9KzZ8/qzoYQQgh7YSe1KNaQeWCEEEIIYbUPP/yQli1b4uzsTPfu3dm1a5fFc5cvX16sFaC86+lJACOEEEIIq6xevZqYmBimTZvG3r176dChA/369ePSpUsWr3F1dSU9Pd20nT17tlz3lABGCCGEEFZ5++23GTlyJM899xzBwcEsWrSI+vXrs3TpUovXaDQavL29TZuXl1e57ikBjBBCCCGKycrKMttycnJKPO/OnTv89NNP9O3b17RPq9XSt29fduzYYTH969ev4+/vj5+fHwMHDuTQoUPlyp8EMEIIIUStkmuDDfz8/HBzczNtb7zxRol3++OPP9Dr9cVqULy8vMjIyCjxmsDAQJYuXcq///1vPv/8cwwGA2FhYZw/f77MTymjkIQQQghRzLlz53B1dTV9dnJyslnaoaGhZtN1hIWFERQUxMcff8ysWbPKlIYEMEIIIUStkpe/WXO9sZNt4QDGkiZNmqDT6bh48aLZ/osXL+Lt7V2mO9arV4+QkBBOnDhR5lxWaxPSwoULad++vamQQkND2bhxo+n4yZMneeyxx/D09MTV1ZXBgwcXK6CSXLhwgSFDhvCXv/wFFxcX2rVrx549e0zHlVK89tpr+Pj44OLiQt++fTl+/HilPKMQQghRtWzThFRWjo6OdO7cmc2bN5v2GQwGNm/eXOZJUfV6PQcOHMDHx6fM963WAMbX15c5c+bw008/sWfPHnr37m3qyHPjxg2ioqLQaDQkJiaSkpLCnTt3eOSRRzAYDBbTvHr1KuHh4dSrV4+NGzdy+PBh5s+fj4eHh+mcefPmsWDBAhYtWkRqaioNGjSgX79+3L59uyoeWwghhKhVYmJi+OSTT1ixYgVHjhxh9OjR3Lhxg+eeew6AYcOGERsbazp/5syZbNq0iVOnTrF3716GDBnC2bNnGTFiRNlvqmoYDw8PtWTJEhUfH6+0Wq3KzMw0Hbt27ZrSaDQqISHB4vWTJk1SERERFo8bDAbl7e2t3nzzTbN0nZyc1KpVq8qcz8zMTAWY5U8IIYQoSVW8MwruAacVXLZiO12hvL7//vuqRYsWytHRUXXr1k3t3LnTdOz+++9X0dHRps/jx483nevl5aUeeughtXfv3nLdr8aMQtLr9Xz11VfcuHGD0NBQ09o/hTsNOTs7o9VqSU5OtpjOhg0b6NKlC0899RRNmzYlJCSETz75xHT89OnTZGRkmA33cnNzo3v37qUO98rJySk2pEwIIYSoefKwrvmoYv1nxowZw9mzZ8nJySE1NZXu3bubjiUlJbF8+XLT53feecd0bkZGBt999x0hISHlul+1d+I9cOAAoaGh3L59m4YNG7Ju3TqCg4Px9PSkQYMGTJo0ibi4OJRSTJ48Gb1eT3p6usX0Tp06xcKFC4mJiWHKlCns3r2bsWPH4ujoSHR0tGlIV3mGewG88cYbzJgxo9h+CWSEEEL8mYJ3hVJ1YJGiKlLtAUxgYCD79+8nMzOTb775hujoaLZu3UpwcDBr1qxh9OjRLFiwAK1WyzPPPEOnTp3Qai1XHBkMBrp06UJcXBwAISEhHDx4kEWLFhEdHV3hfMbGxhITE2P6fOHCBYKDg/Hz86twmkIIIeqW7Oxs3NzcKvku5e+IW/z6mq/aAxhHR0fuueceADp37szu3bt57733+Pjjj4mKiuLkyZP88ccfODg44O7ujre3N61atbKYno+PD8HBwWb7goKCWLt2LYBpSNfFixfNejtfvHiRjh07WkzXycnJrDmrYcOGnDt3jkaNGqHRaMr93GWRlZWFn59fsbH4QsrGEikXy6RsLJOyKZkty0UpRXZ2Ns2aNbNR7kpjm2HUNV21BzBFGQyGYtMVN2nSBIDExEQuXbrEo48+avH68PBw0tLSzPYdO3YMf39/AAICAvD29mbz5s2mgCUrK4vU1FRGjx5d5nxqtVp8fX3LfL41yjoWvy6SsimZlItlUjaWSdmUzFblUvk1L3VLtQYwsbGx9O/fnxYtWpCdnc2XX35JUlIS8fHxACxbtoygoCA8PT3ZsWMH48aNY8KECQQGBprS6NOnD4899hhjxowBYMKECYSFhREXF8fgwYPZtWsXixcvZvHixYBx8ajx48fz+uuv06ZNGwICAvi///s/mjVrxqBBg6q8DIQQQgjbKujEa831NV+1BjCXLl1i2LBhpKen4+bmRvv27YmPjycyMhKAtLQ0YmNjuXLlCi1btmTq1KlMmDDBLI2CJqYCXbt2Zd26dcTGxjJz5kwCAgJ49913efbZZ03nTJw4kRs3bjBq1CiuXbtGREQE//3vf3F2dq6aBxdCCCEqjTQhVbpPP/201ONz5sxhzpw5pZ5z5syZYvsGDBjAgAEDLF6j0WiYOXMmM2fOLFM+q4uTkxPTpk2z6foTtYWUTcmkXCyTsrFMyqZk9lsudaMTr0bJmC4hhBDC7mVlZeX3s0kBGlqR0nUgnMzMzBrdJ6rGdeIVQgghhDWkCUkIIYQQdqdudOKtMUsJCCGEEEKUldTACCGEELVK3WhCkhqYKjR79mzCwsKoX78+7u7uJZ7z66+/8vDDD1O/fn2aNm3KP//5T/Ly7n4zJSUlodFoim2lreN0+/Zthg8fTrt27XBwcKiR891UV9kA/PLLL/Ts2RNnZ2f8/PyYN2+eLR/NKrYol8JSUlJwcHAoddbpAl9//TUdO3akfv36+Pv78+abb1rxJLZXnWUTHx9Pjx49aNSoEZ6enjzxxBMljoisLtVVNtOnTy/xZ7BBgwZWPpFtVOf3jFKKt956i7Zt2+Lk5ETz5s2ZPXu2FU9TGmsWcrR2BFPVkRqYKnTnzh2eeuopQkNDSxxCrtfrefjhh/H29mb79u2kp6czbNgw6tWrZ1rbqUBaWppZ7/CmTZtavK9er8fFxYWxY8eallSoaaqrbLKysoiKiqJv374sWrSIAwcO8Pzzz+Pu7s6oUaNs94AVZMtyuXbtGsOGDaNPnz5cvHix1Ptu3LiRZ599lvfff5+oqCiOHDnCyJEjcXFxMU0aWd2qq2xOnz7NwIEDiYmJ4YsvviAzM5MJEybw+OOPs3fvXps+Y0VVV9m88sorvPjii2b7+vTpQ9euXa1/KBuornIBGDduHJs2beKtt96iXbt2XLlyhStXrtjs2eokJarcsmXLlJubW7H933//vdJqtSojI8O0b+HChcrV1VXl5OQopZTasmWLAtTVq1crdO/o6Gg1cODACl1bFaq6bD766CPl4eFhSkMppSZNmqQCAwMr/AyVwZpyKfC3v/1Nvfrqq2ratGmqQ4cOpd7vmWeeUU8++aTZvgULFihfX19lMBgq/ByVoarLZs2aNcrBwUHp9XrTvg0bNiiNRqPu3Llj1bPYWlWXTVH79+9XgPrxxx8rkv1KU9XlcvjwYeXg4KCOHj1qi+xblJmZqQAF6xUkWLGtV4DKzMys1PxaS5qQapAdO3bQrl07vLy8TPv69etHVlYWhw4dMju3Y8eO+Pj4EBkZSUpKSlVntcpVVtns2LGD++67D0dHR7N009LSuHr1qm0fohKUtVyWLVvGqVOnmDZtWpnSzcnJKTYztYuLC+fPn+fs2bO2yXwlq6yy6dy5M1qtlmXLlqHX68nMzOSzzz6jb9++1KtXz+bPURkqq2yKWrJkCW3btqVnz55W57kqVFa5/Oc//6FVq1Z8++23BAQE0LJlS0aMGFGJNTAFo5AqukkfGFFOGRkZZj84gOlzQT8OHx8fFi1axNq1a1m7di1+fn706tWrxlRdV5bKKpuypFuTlSX/x48fZ/LkyXz++ec4OJSt1bhfv37861//YvPmzRgMBo4dO8b8+fMBSE9Pt+ETVJ7KKpuAgAA2bdrElClTcHJywt3dnfPnz/P111/b9gEqUWWVTWG3b9/miy++4IUXXrA+w1Wkssrl1KlTnD17ljVr1rBy5UqWL1/OTz/9xJNPPmnbB6hjJICx0uTJk0vstFZ4O3r0qM3uFxgYyP/+7//SuXNnwsLCWLp0KWFhYbzzzjs2u4etSNmUrCrLRa/X8/e//50ZM2bQtm3bMl83cuRIxowZw4ABA3B0dKRHjx48/fTTgHEl9spiD2WTkZHByJEjiY6OZvfu3WzduhVHR0eefPJJVCVObG4PZVPYunXryM7OJjo62iZ5ssQeysVgMJCTk8PKlSvp2bMnvXr14tNPP2XLli2kpaXZJG/m8myw1XzSiddKL7/8MsOHDy/1nFatWpUpLW9vb3bt2mW2r6BzmLe3t8XrunXrRnJycpnuUZXsoWy8vb2LdcArS7rWqMpyyc7OZs+ePezbt8/U+dZgMKCUwsHBgU2bNtG7d+9i6Wo0GubOnUtcXBwZGRl4enqyefPmcuWtIuyhbD788EPc3NzMRqt9/vnn+Pn5kZqaSo8ePcqUv/Kyh7IpbMmSJQwYMKBYjYat2UO5+Pj44ODgYBb0BAUFAcZRT4GBgWXKX9nVjbWQJICxkqenJ56enjZJKzQ0lNmzZ3Pp0iXTyJmEhARcXV0JDg62eN3+/fvx8fGxSR5syR7KJjQ0lKlTp5Kbm2vqv5CQkEBgYCAeHh42yXtRVVku9erV48CBA2bXfPTRRyQmJvLNN98QEBBQavo6nY7mzZsDsGrVKkJDQ22W95LYQ9ncvHmzWC2UTqcDjC+zymIPZVPg9OnTbNmyhQ0bNtgkv6Wxh3IJDw8nLy+PkydP0rp1awCOHTsGgL+/v03ybq5uBDAyCqkKnT17Vu3bt0/NmDFDNWzYUO3bt0/t27dPZWdnK6WUysvLU/fee6+KiopS+/fvV//973+Vp6enio2NNaXxzjvvqPXr16vjx4+rAwcOqHHjximtVqt++OEH0znvv/++6t27t9m9Dx06pPbt26ceeeQR1atXL9O9a4rqKptr164pLy8vNXToUHXw4EH11Vdfqfr166uPP/646h6+FLYol6JKGjVRtFx+//13tXDhQnXkyBG1b98+NXbsWOXs7KxSU1Mr5TkrorrKZvPmzUqj0agZM2aoY8eOqZ9++kn169dP+fv7q5s3b1bKs5ZXdZVNgVdffVU1a9ZM5eXl2fS5rFVd5aLX61WnTp3Ufffdp/bu3av27NmjunfvriIjI236fHdHIS1V8JUV21K7GIUkAUwVio6Ozv/mMt+2bNliOufMmTOqf//+ysXFRTVp0kS9/PLLKjc313R87ty5qnXr1srZ2Vk1btxY9erVSyUmJprdZ9q0acrf399sn7+/f4n3rimqs2x+/vlnFRERoZycnFTz5s3VnDlzKvNRy8UW5VJUSb9wi5bL77//rnr06KEaNGig6tevr/r06aN27txp46ezTnWVjVJKrVq1SoWEhKgGDRooT09P9eijj6ojR47Y8OmsU51lo9frla+vr5oyZYoNn8g2qrNcLly4oB5//HHVsGFD5eXlpYYPH64uX75sw6crHMAsVvC5FdtiuwhgNEpVYq8zIYQQQlSJrKws3NzcgI8AFytSugW8RGZmptmkoDWNjEISQgghhN2RTrxCCCFErVI3FnOUAEYIIYSoVXKx7vVuH6OQpAlJCCGEEHZHamCEEEKIWkWakIQQQghhdwoWc7Tm+ppPmpCEEEIIYXekBkYIIYSoVepGE5LUwAghbObMmTNoNBr2799fKelrNBrWr19fKWkLUXvk2mCr+SSAEaIWGT58OIMGDaq2+/v5+ZGens69994LQFJSEhqNhmvXrlVbnoSoe/JssNV80oQkhLAZnU6Ht7d3dWdDiDoup5qvrxpSAyNEHbF161a6deuGk5MTPj4+TJ48mby8u39p9erVi7FjxzJx4kQaN26Mt7c306dPN0vj6NGjRERE4OzsTHBwMD/88INZs07hJqQzZ87wwAMPAODh4YFGo2H48OEAtGzZknfffdcs7Y4dO5rd7/jx49x3332meyUkJBR7pnPnzjF48GDc3d1p3LgxAwcO5MyZM9YWlRB2ydHRMf8PiHeAOVZs7+Dt7Y2jo2OVP0N5SA2MEHXAhQsXeOihhxg+fDgrV67k6NGjjBw5EmdnZ7OgYcWKFcTExJCamsqOHTsYPnw44eHhREZGotfrGTRoEC1atCA1NZXs7Gxefvlli/f08/Nj7dq1PPHEE6SlpeHq6oqLS9kWmDMYDDz++ON4eXmRmppKZmYm48ePNzsnNzeXfv36ERoayrZt23BwcOD111/nwQcf5Jdffqnxv3yFsDVnZ2dOnz7NnTt3rE7L0dERZ2dnG+Sq8kgAI0Qd8NFHH+Hn58cHH3yARqPhr3/9K7/99huTJk3itddeQ6s1Vsa2b9+eadOmAdCmTRs++OADNm/eTGRkJAkJCZw8eZKkpCRTM9Hs2bOJjIws8Z46nY7GjRsD0LRpU9zd3cuc3x9++IGjR48SHx9Ps2bNAIiLi6N///6mc1avXo3BYGDJkiVoNBoAli1bhru7O0lJSURFRZWvkISoBZydnWt84GErEsAIUQccOXKE0NBQ04seIDw8nOvXr3P+/HlatGgBGAOYwnx8fLh06RIAaWlp+Pn5mfVx6datW6Xl18/PzxS8AISGhpqd8/PPP3PixAkaNWpktv/27ducPHmyUvIlhKg5JIARQpjUq1fP7LNGo8FgMNj8PlqtFqWU2b7c3PIN3bx+/TqdO3fmiy++KHbM09PTqvwJIWo+CWCEqAOCgoJYu3YtSilTLUxKSgqNGjXC19e3TGkEBgZy7tw5Ll68iJeXFwC7d+8u9ZqCfih6vd5sv6enJ+np6abPWVlZnD592iy/586dIz09HR8fHwB27txplkanTp1YvXo1TZs2xdXVtUzPIISoPWQUkhC1TGZmJvv37zfbRo0axblz5/jHP/7B0aNH+fe//820adOIiYkx9X/5M5GRkbRu3Zro6Gh++eUXUlJSePXVVwHMmqYK8/f3R6PR8O233/L7779z/fp1AHr37s1nn33Gtm3bOHDgANHR0eh0OtN1ffv2pW3btkRHR/Pzzz+zbds2pk6dapb2s88+S5MmTRg4cCDbtm3j9OnTJCUlMXbsWM6fP1+RohNC2BEJYISoZZKSkggJCTHbZs2axffff8+uXbvo0KEDL774Ii+88IIpACkLnU7H+vXruX79Ol27dmXEiBGmoMJSp8HmzZszY8YMJk+ejJeXF2PGjAEgNjaW+++/nwEDBvDwww8zaNAgWrdubbpOq9Wybt06bt26Rbdu3RgxYgSzZ882S7t+/fr8+OOPtGjRgscff5ygoCBeeOEFbt++LTUyQtQBGlW0IVoIIcooJSWFiIgITpw4YRaACCFEZZMARghRZuvWraNhw4a0adOGEydOMG7cODw8PEhOTq7urAkh6hjpxCuEKLPs7GwmTZrEr7/+SpMmTejbty/z58+v7mwJIeogqYERQgghhN2RTrxCCCGEsDsSwAghhBDC7kgAI4QQQgi7IwGMEEIIIeyOBDBCCCGEsDsSwAghhBDC7kgAI4QQQgi7IwGMEEIIIezO/wcgWdQPe4uYUAAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"source":"## Summary","metadata":{},"id":"fcfcf615-56e6-4dc8-b919-41e4c21d7cec","cell_type":"markdown"},{"source":"Coffee shop location recommendation:\n\n    1. Capitol Hill\n    \n    2. North Capitol Hill\n    \n    3. Speer","metadata":{},"cell_type":"markdown","id":"d2fc7747-139b-4fcc-b1d4-b066e35afe3f"}],"metadata":{"editor":"DataCamp Workspace","kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.10"}},"nbformat":4,"nbformat_minor":5}