Switch branches/tags
Nothing to show
Find file History
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
..
Failed to load latest commit information.
visuals
2016-fal-ggelinas-poster.pdf
DistrictNumCrimes.py
HospitalLocationAnalysis.py
PropertyCrimeAnalysis.py
PropertyMean.py
README.md
getData.py
getGeoJSON.py
heatmap.html
hospital.html
hospitalmap.html
visual.py
web.py

README.md

#Boston Crime Rates Relations

##Introduction In this project we used data from the city of Boston to try and understand crime rates and their relation to hospitals, police districts, and property value. We wanted to determine:

  1. If crime was more likely to happen in areas with lower property values
  2. If hospitals are located around crime areas

The data sets being used for this analysis are acquired from the city of Boston's data portal. The techniques that we used in this project are correlation coefficient and k-means clustering. With these techniques, we hope to find out if crime is more likely to occur in areas with lower property value and if hospitals are strategically located closer to areas with high crime rates.

##Data Sets

###Boston Police Stations This data set contains information about the 12 police districts within the city of Boston. It includes the name of each police district and their respective district number. It also provides addresses, geographical coordinates, and zip codes.

###Boston Hospitals This data set contains information about hospitals within the city of Boston. It contains hospital names, geographical coordinates, addresses, and zip codes.

###Boston Crime Reports This data set is from the city of Boston's police department and contains incident reports from August 2015 to the present day. Each relation in this data set consists of an incident number, offense code, offense description, district, date, street address and geographical coordinates. The data set that was used for this analysis included crime reports from August 2015 until 18 November 2016.

###Boston Property Assessments This data set contains the tax assessment of property for 2016. Each entry in this data set contains an address, zip code, owner, mailing address, average land value, average building value, average total value, gross tax, as well as other descriptors of the property.

##Description Using the data sets above, I wanted to observe if there was a correlation between the number of crimes in a police district and the average property value within that district. My hypothesis is that there should be a negative correlation between property value and crime rate. As property value increase I expect for the number of crimes to decrease. I will be using the Boston Police Stations, Boston Crime Reports, and Property Assessments data sets to calculate the correlation coefficient and p-value of the relationship between crime and property value. I also wanted to see if hospitals are located near areas with more crime. I hypothesized that it would be so that ambulances and emergency services would be able to respond to emergencies more quickly. There is no auth.json file since I did not use any keys to access any of the data sets used in this project.

Heat Map of Crimes from August 2015 - November 2016 Heat Map of Crime

##Transformations

  1. getData.py obtains all of the data sets and stores them as collections within MongoDB.

  2. DistrictNumCrimes.py takes in Boston Crime Reports and Boston Police Stations data sets and it computes the total number of crimes for each district from the Boston Crime Reports and adds a new column within the Boston Police Stations data set called num_crimes that corresponds to its specific district.

  3. PropertyMean.py takes the Boston Property Assessment and Boston Police Stations data sets and transforms them to obtain the average property value within a 1 mile range from each police district. I used a package called pyzipcode that must be installed in order to run this file. pyzipcode provides a database of zipcodes and functions that provide nearby zip codes within a range of a specific zipcode you are focused on. Although the full installation of pyzipcode might not fully install it should be able to still install pyzipcode without sqlite3.

##Correlation of Property Value and Crime In order to determine a relationship between property value and crime rates, a method that could be used is correlation. Correlation coefficient is a measure that determines the degree to which two variables movements are associated/related. The range of values for the correlation coefficient are between -1 and 1. From computing the coefficient value it can yield a positive, negative or no correlation relationship. A positive correlation is when one variable increases along with the other variable or one variable decreases along with the other variable. While a negative correlation indicates that when one variable increases the other variable decreases. As for a 0 correlation coefficient, this means that there is no relationship between those two variables.

Before performing the analysis, we predicted that there would be a negative correlation. As property value increases then crime rates would decrease. We assumed that property value increases in regards to safety leading to less crimes within that area. In addition, we assumed that low income areas may have more crimes as a means to afford living. We decided to group property values and crimes within the police districts. These groupings will help provide samples from different areas around boston in order to calculate correlation. Property values however do not have an attribute for police districts. In order to account for this we decided to use a python package called pyzipcode which would take the zip codes from each entry in the property data set and it will find the nearest police district within its one mile radius range. Once found the property value will be added to the closest police district. After iterating through the property data set, we computed the average for all property value in each district. Using the average property value and sum of crimes for each district, we applied the pearson correlation coefficient algorithm and used multiple permutations.

There were difficulties in mapping property value in certain districts because of the lack of real boundaries between the districts. I compromised and computed the groupings by taking a list of zipcodes within a 1 mile radius of the property and matching that zip code with the police districts zip code.

Based on the calculations it yielded these results.

Correlation Coefficient: -0.3138711 P-value: 1.0

Property and Crime Correlation graph

This correlation coefficient shows that a moderate/weak negative correlation. With a high p-value, it states that this observation is a non-significant result that will not prove the Null hypothesis false. This suggests that the Null hypothesis: "Crimes decreasing and property value increasing" may be true.

##Hospital Location Analysis Another inquiry is to determine if hospitals are optimally located against crime. We believe that hospitals may be located around areas with crime in order to reduce time and increase efficiency in treating victims. We decided to use the k-means algorithm to compute the difference. The k-means algorithm takes in geographical coordinates from the crimes data set and clusters points to form k clusters determined by the input. For this experiment, we used the number of hospitals in the hospitals data set as the k value in order to compare distances. After running the k-means algorithm and computing the difference in distance between current hospitals and optimal hospitals it clearly showed very little difference.

Here are the results:

Locations of current hospital locations: [(42.30025000839615, -71.10737910445549), (42.3438499996779, -71.08983000035408), (42.31856289432221, -71.09165569529381), (42.329611374844326, -71.10616871232227), (42.27137912172521, -71.08168028446168), (42.3371094801158, -71.07139912234962), (42.329611374844326, -71.10616871232227), (42.36297141612903, -71.07043169540236), (42.34665771451756, -71.14136122385321), (42.33587602903896, -71.10741054246668), (42.27854306401838, -71.06631280050811), (42.349946522039204, -71.0634111017112), (42.30021828265608, -71.12789683059322), (42.349656455743144, -71.14822103232248), (42.27598935537618, -71.17245195460838), (42.36327718561898, -71.0668523937257), (42.337592548462226, -71.10472284437952), (42.31617666213941, -71.11272670363542), (42.29738386053219, -71.13150465441208), (42.314030311294516, -71.06406449543488), (42.36764789068138, -71.06564730220646), (42.352620000312925, -71.13281000028115), (42.3385289546495, -71.10940050507557), (42.33734993862189, -71.1071702648531), (42.36247485742686, -71.06924724545246), (42.335925371008436, -71.07378404269969)]

K means hospital locations: [(42.31766665432098, -71.08874154320986), (42.335336941176465, -71.06332752941175), (42.2758958548387, -71.09411869354837), (42.28982995238095, -71.10348738095237), (42.3534265, -71.0765595), (42.34568411111111, -71.14137922222223), (42.33503033333333, -71.10632700000001), (39.704630671641794, -66.88389731343281), (42.3537191, -71.13231374999998), (42.31140083760684, -71.0637535213675), (42.334351783333325, -71.07737868333335), (42.340216, -71.109561), (42.37631418367348, -71.04442244897959), (42.34869166666667, -71.05903538562089), (42.296395600000004, -71.1229084), (42.3494088, -71.15434520000002), (42.26494976923077, -71.15218800000001), (42.36074204166666, -71.06050870833333), (42.336705375, -71.09906812499999), (42.316876400000005, -71.11061939999999), (42.27484629166666, -71.13036083333334), (42.327336736842106, -71.10366789473683), (42.344890825, -71.08917249999999), (42.355409333333334, -71.07107333333333)]

Difference between hospitals and optimal K means clusters: [(0.017416645924832608, 0.01863756124562599), (0.008513058501435466, 0.026502470942332934), (0.04266703948351136, 0.0024629982545576468), (0.03978142246337768, 0.0026813313698994534), (0.08204737827478681, 0.005120784461681183), (0.008574630995312305, 0.069980099872609), (0.005418958489002534, 0.00015828767773484742), (2.6583407444872336, 4.186534381969551), (0.007061385482437288, 0.009047473853229349), (0.02447519143211707, 0.043657021099178905), (0.05580871931494613, 0.011065882825235462), (0.00973052203920588, 0.046149898288803115), (0.07609590101739627, 0.08347438161362675), (0.0009647890764767908, 0.08918564670159412), (0.020406244623821124, 0.049543554608376894), (0.013868385618984291, 0.08749280627432654), (0.07264277923145812, 0.047465155620486144), (0.04456537952724915, 0.052217995302086706), (0.03932151446780807, 0.03243652941209518), (0.0028460887054890804, 0.04655490456511302), (0.09280159901472018, 0.06471353112688405), (0.02528326347081844, 0.029142105544323726), (0.006361870350502841, 0.020228005075580313), (0.018059394711443133, 0.03609693151976501)]

Hospital vs. Optimal Hospital Locations Optimal Hospital locations

  • Red: Current Hospitals
  • Blue: Optimal Hospitals

##Results In our hypothesis, we predicted that we would find a negative correlation between the average property value and the number of crimes in each district. The results displayed a moderate to weak correlation between those two variables in addition to a high p-value. The weak correlation may be a result of not having enough crime data as the sample was only taken from August 2015 through November 2016. There are more data sets of crime incidents provided by Boston and there may be different results in adding other years as well. With this negative correlation, we can assume that these variables may have moderate relationship in affecting each other. The high p-value suggests that it would support the hypothesis.

In regards to hospital locations, computing the distance between hospitals and crime supported our original hypothesis that hospitals are located near crime areas. Shown in the map above, hospitals in downtown Boston show very acute distances. However, stretching out towards the outskirts of the city limits distance does tend to increase between the optimal k-means cluster and the original hospital location.

##Conclusion With these results, we can conclude that there is a small correlation between crime rates and property value. Furthermore we can also see that hospital locations are located near areas of crime. Future work would involve adding more crime data into the correlation algorithm. In addition to adding more crime data from previous years, the data set currently being used is constantly updating with information. It would interesting to see the relationship between property value and crimes over time. As for hospital locations, additional analysis could involve performing the correlation equation between hospitals and low income areas in order to see if there is a relationship between them.

##References City of Boston Data: https://data.cityofboston.gov/

##Setup Initialize mongodb

mongod

Open up mongo shell

mongo

###To collect data

python getData.py

###To perform correlation with property and crime

python DistrictNumCrimes.py
python PropertyMean.py
python PropertyCrimeAnalysis.py

###To perform k-means hospitals

python HospitalLocationAnalysis.py

###To perform visualization You must run all of the previous files first. This file grabs values from the database. Running file will bring up one visual and the rest will be saved as html in the current directory. opening the html will show the interactive maps

python visual.py