[SOLVED] CS scheme Assignment 2: Mapping Parking Violations in NYC

Assignment 2: Mapping Parking Violations in NYC
2021-03-09
Parking Violations in NYC Data
For this assignment, we are going to investigate data on parking violations in NYC.
Parking violations in 2020/21 NYC Open Data has data on all parking violations issued in NYC since 2014. The updated dataset provided for 2021 currently includes about 10 million observations. To make the assignment manageable, I have reduced it to a subset of tickets issued in from Jan 2020 to Jan 2021 and by Manhattan precincts only, yielding about 2.2M tickets.
Two support files are also included in the parking sub folder: the descriptions of all variables
the dictionary of violation codes
Police Precincts A second data source is the shape files of police precincts in NYC.
Exercise
1. Data exploration Before focusing on the spatial part of the data, lets explore the basic patterns in the data.
a) Violation Code and Fine Amounts Add the violation code descriptions and fine amounts to the data file. Provide a visual overview of the top 10 most common types of violations (feel free to group them into categories if reasonable). Compare how this ranking differs if we focus on the total amount of revenue generated.
b) Average amount of fine by vehicle Compare the average amount of fine by vehicle color, vehicle year, and vehicle plate type [Hint: it is sufficient to restrict your attention to commercial (COM) and passenger (PAS) vehicles]? Briefly describe your findings.
c) Effect of COVID Lets see if we can observe the effect of COVID restrictions on parking violations. Present a visualization that shows how parking violations changed after the New York statewide stay-at- home order on March 14, 2020. Make sure the visualization clearly highlights the main pattern (the COVID effect).
2. Map by Precincts Read in the shape files for the police precincts and remove all precincts outside of Manhattan.
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a) Number of tickets, total fines, and average fines Provide three maps that show choropleth maps of:
the total number of tickets the total amount of fines
the average amount of fines
Briefly describe what you learn from these maps in comparison.
b) Types of violations Group the almost 100 types of ticket violations into a smaller set of 4-6 subgroups (where other should be the remainder of violations not included in other groups you defined). [Hint: No need to spend more than 5 minutes thinking about what the right grouping is.]. Provide choropleth maps for each of these subgroups to show where different types of violations are more or less common.
3. Focus on the Upper East Precinct 19 identifies the Upper East Side. The data currently does not provide latitude and longitude of the violation locations (and I am not sure what these street_code variables are for).
a) Ignoring fire hydrants Restrict your data to parking violations related to fire hydrants (Violation Code = 40). Using the variables Street Name and House Number as well as the knowledge that these addresses are in the Upper East Side of Manhattan, geocode at least 500 addresses. Include a data table of these addresses and the latitude and longitude of these addresses in the output.
b) Interactive Map Provide an interactive map of the violations you geocoded using leaflet. Provide at least three pieces of information on the parking ticket in a popup.
c) Luxury cars and repeat offenders Using the vehicle Plate ID, identify repeat offenders (in the full dataset). Create another variable called luxury_car in which you identify luxury car brands using the Vehicle Make variable.
Start with the previous map. Distinguish the points by whether the car is a repeat offender and/or luxury car. Add a legend informing the user about the color scheme. Also make sure that the added information about the car type and repeat offender status is now contained in the popup information. Show this map.
d) Cluster Add marker clustering, so that zooming in will reveal the individual locations but the zoomed out map only shows the clusters. Show the map with clusters.
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