Retrieved and included income data from ACS; corrected unit count totals

check-extremes.py

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+import sqlite3
+
+years = [
+    2013,
+    2014,
+    2015,
+    2016,
+    2017,
+    2018,
+    2019
+]
+# Future: add code to add the current year to the list if it's not already there
+
+database = sqlite3.connect("permit_data.db")
+cursor = database.cursor()
+
+resultsFile = open("extremes.txt", "w")
+
+for year in years:
+    resultsFile.write("----------------------------\n")
+    resultsFile.write("Year: " + str(year) + "\n\n")
+
+    # ZIP code with minimum
+    # command = """
+    #     SELECT ZIP, MIN(%s)
+    #     FROM statistics;
+    # """ % ("Units" + str(year))
+
+    command = """
+        SELECT ZIP, %s, MedIncome, Description
+        FROM statistics
+        WHERE %s = 0;
+    """ % ("Units" + str(year), "Units" + str(year))
+
+    cursor.execute(command)
+    resultsFile.write("ZIPs with zero units:\n")
+    for result in cursor:
+        resultsFile.write("ZIP " + str(result[0]) + ", " + str(result[3]) + " \n")
+        resultsFile.write("\t- Income: $" + str(result[2]) + "\n")
+        # resultsFile.write("\n")
+        # resultsFile.write("Min ZIP units: " + str(result[1]) + "\n\n")
+
+    # ZIP code with maximum
+    command = """
+        SELECT ZIP, MAX(%s), MedIncome, Description
+        FROM statistics;
+    """ % ("Units" + str(year))
+
+    cursor.execute(command)
+    resultsFile.write("\n")
+    for result in cursor:
+        resultsFile.write("Max: ZIP " + str(result[0]) + ", " + str(result[3]) + " \n")
+        resultsFile.write("\t- Income: $" + str(result[2]) + "\n")
+        resultsFile.write("Max ZIP units: " + str(result[1]) + "\n\n")
+
+    # checked data and found that maximums were never matched
+
+database.commit()
+database.close()
+resultsFile.close()

create-statistics-table.py

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+import sqlite3
+import csv
+
+connection = sqlite3.connect("permit_data.db")
+cursor = connection.cursor()
+
+# completely eliminate the table so you can recreate it
+cursor.execute("DROP TABLE statistics;")
+
+command = """
+CREATE TABLE statistics (
+    ZIP int PRIMARY KEY,
+    UnitsAll int,
+    Units2019 int,
+    Units2018 int,
+    Units2017 int,
+    Units2016 int,
+    Units2015 int,
+    Units2014 int,
+    Units2013 int,
+    MedIncome int,
+    Description varchar(50));"""
+
+cursor.execute(command)
+
+ZIPfile = open("zip-codes.txt", "r")
+
+IncomeFile = open("median-income-2017.csv", "r")
+IncomeReader = csv.DictReader(IncomeFile, delimiter=",")
+IncomeDict = {}
+
+DescFile = open("la-county-master-zips.csv", "r")
+DescReader = csv.DictReader(DescFile, delimiter=",")
+DescDict = {}
+
+for row in IncomeReader:
+    IncomeDict[row["zip-code"]] = row["income"]
+
+for row in DescReader:
+    DescDict[row["\xef\xbb\xbfzip-code"]] = row["area"] # not sure why all the extra characters but there they are
+
+for ZIP in ZIPfile:
+    Income = IncomeDict[ZIP.strip()]
+    Desc = DescDict[ZIP.strip()]
+    cursor.execute("INSERT INTO statistics (ZIP, MedIncome, Description) VALUES (%s, %s, '%s');" % (ZIP, Income, Desc))
+
+IncomeFile.close()
+DescFile.close()
+ZIPfile.close()
+
+connection.commit()
+connection.close()

extremes.txt

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+----------------------------
+Year: 2013
+
+ZIPs with zero units:
+ZIP 90042, Highland Park (City of LA)
+	- Income: $55596
+ZIP 90036, Park La Brea (City of LA)
+	- Income: $74279
+ZIP 91605, North Hollywood
+	- Income: $43004
+ZIP 91325, Northridge (City of LA)
+	- Income: $65129
+ZIP 90025, Sawtelle (City of LA)/West Los Angeles (City of LA)
+	- Income: $78713
+ZIP 91345, Mission Hills (City of LA)
+	- Income: $74193
+ZIP 91324, Northridge (City of LA)
+	- Income: $68477
+ZIP 91307, West Hills (City of LA)
+	- Income: $111043
+ZIP 91402, Panorama City (City of LA)
+	- Income: $41669
+ZIP 91311, Chatsworth (City of LA)
+	- Income: $82738
+ZIP 91606, North Hollywood
+	- Income: $44773
+ZIP 90026, Echo Park/Silverlake (City of LA)
+	- Income: $54342
+ZIP 90043, Hyde Park (City of LA)/View Park/Windsor Hills
+	- Income: $41812
+ZIP 91304, Canoga Park (City of LA)
+	- Income: $64367
+ZIP 90041, Eagle Rock (City of LA)
+	- Income: $73250
+ZIP 91331, Arleta (City of LA)/Pacoima (City of LA)
+	- Income: $57353
+ZIP 90004, Hancock Park (City of LA)
+	- Income: $46581
+ZIP 91411, Van Nuys (City of LA)
+	- Income: $50332
+ZIP 90744, Wilmington (City of LA)
+	- Income: $43284
+ZIP 90032, El Sereno (City of LA)/Monterey Hills (City of LA)
+	- Income: $47370
+ZIP 90020, Hancock Park (City of LA)
+	- Income: $42407
+ZIP 90501, Torrance
+	- Income: $64995
+ZIP 90031, Montecito Heights (City of LA)
+	- Income: $41126
+ZIP 91364, Woodland Hills (City of LA)
+	- Income: $100323
+ZIP 91303, Canoga Park (City of LA)
+	- Income: $51486
+ZIP 90732, Rancho Palos Verdes
+	- Income: $89440
+ZIP 90005, Koreatown (City of LA)
+	- Income: $32461
+ZIP 90292, Marina del Rey
+	- Income: $103948
+ZIP 90717, Lomita/Rancho Palos Verdes
+	- Income: $61077
+
+Max: ZIP 90015, Downtown Los Angeles (City of LA)
+	- Income: $32979
+Max ZIP units: 348
+
+----------------------------
+Year: 2014
+
+ZIPs with zero units:
+ZIP 91307, West Hills (City of LA)
+	- Income: $111043
+ZIP 91306, Winnetka (City of LA)
+	- Income: $64772
+ZIP 91343, North Hills (City of LA)
+	- Income: $61692
+ZIP 90020, Hancock Park (City of LA)
+	- Income: $42407
+ZIP 90717, Lomita/Rancho Palos Verdes
+	- Income: $61077
+
+Max: ZIP 90013, Downtown Los Angeles (City of LA)
+	- Income: $22808
+Max ZIP units: 159
+
+----------------------------
+Year: 2015
+
+ZIPs with zero units:
+ZIP 90020, Hancock Park (City of LA)
+	- Income: $42407
+ZIP 91504, Burbank (Glenoaks)
+	- Income: $73501
+ZIP 90008, Baldwin Hills/Crenshaw (City of LA)/Leimert Park (City of LA)
+	- Income: $36641
+ZIP 91340, San Fernando
+	- Income: $54703
+
+Max: ZIP 90015, Downtown Los Angeles (City of LA)
+	- Income: $32979
+Max ZIP units: 1526
+
+----------------------------
+Year: 2016
+
+ZIPs with zero units:
+ZIP 91345, Mission Hills (City of LA)
+	- Income: $74193
+ZIP 91307, West Hills (City of LA)
+	- Income: $111043
+ZIP 90063, City Terrace
+	- Income: $44121
+ZIP 91205, Glendale (Tropico)
+	- Income: $38730
+ZIP 90247, Gardena
+	- Income: $46076
+ZIP 91214, La Crescenta
+	- Income: $94322
+
+Max: ZIP 90094, Playa Vista
+	- Income: $136250
+Max ZIP units: 1844
+
+----------------------------
+Year: 2017
+
+ZIPs with zero units:
+ZIP 91504, Burbank (Glenoaks)
+	- Income: $73501
+ZIP 90017, Downtown Los Angeles (City of LA)
+	- Income: $28638
+
+Max: ZIP 90015, Downtown Los Angeles (City of LA)
+	- Income: $32979
+Max ZIP units: 1821
+
+----------------------------
+Year: 2018
+
+ZIPs with zero units:
+ZIP 91504, Burbank (Glenoaks)
+	- Income: $73501
+ZIP 91505, Burbank
+	- Income: $82630
+
+Max: ZIP 91601, North Hollywood (City of LA)
+	- Income: $50832
+Max ZIP units: 937
+
+----------------------------
+Year: 2019
+
+ZIPs with zero units:
+ZIP 91307, West Hills (City of LA)
+	- Income: $111043
+ZIP 90020, Hancock Park (City of LA)
+	- Income: $42407
+ZIP 91335, Reseda (City of LA)
+	- Income: $55580
+ZIP 90031, Montecito Heights (City of LA)
+	- Income: $41126
+ZIP 90067, Century City (City of LA)
+	- Income: $123889
+ZIP 90732, Rancho Palos Verdes
+	- Income: $89440
+ZIP 90710, Harbor City (City of LA)
+	- Income: $57968
+ZIP 91504, Burbank (Glenoaks)
+	- Income: $73501
+ZIP 90057, Westlake (City of LA) 
+	- Income: $31337
+ZIP 90013, Downtown Los Angeles (City of LA)
+	- Income: $22808
+
+Max: ZIP 90036, Park La Brea (City of LA)
+	- Income: $74279
+Max ZIP units: 287

factfinder-data/ACS_17_5YR_B01003.txt

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+B01003
+TOTAL POPULATION
+
+Universe: Total population
+
+Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties.
+
+
+Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.
+
+Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.
+
+
+Source:  U.S. Census Bureau, 2013-2017 American Community Survey 5-Year Estimates
+
+
+Explanation of Symbols:An '**' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.
+An '-' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.
+An '-' following a median estimate means the median falls in the lowest interval of an open-ended distribution.
+An '+' following a median estimate means the median falls in the upper interval of an open-ended distribution.
+An '***' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate.
+An '*****' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. 
+An 'N' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small.
+An '(X)' means that the estimate is not applicable or not available.
+
+
+Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data).  The effect of nonsampling error is not represented in these tables.
+
+
+While the 2013-2017 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities.
+
+
+Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization.
+
+

factfinder-data/ACS_17_5YR_B01003_metadata.csv

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+GEO.id,Id
+GEO.id2,Id2
+GEO.display-label,Geography
+HD01_VD01,Estimate; Total
+HD02_VD01,Margin of Error; Total