ZIP Code boundaries in Chicago. This dataset is in a format for spatial datasets that is inherently tabular but allows for a map as a derived view. Please click the indicated link below for such a map. To export the data in either tabular or geographic format, please use the Export button on this dataset.

Map of ZIP Code boundaries in Chicago.

ZIP Code boundaries in Chicago. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ).

A dataset listing Illinois zip codes by population for 2024.

This zip code layer was constructed by Nevada County GIS from voter registration rolls and Assessor Mailing Addresses. It is the best available data, but boundaries are not precise. Zip Codes 95712 (Chicago Park) and 95924 (Cedar Ridge) are for the US Post Office and PO boxes only. In Truckee, there is a 96160 Zip Code that is for PO Boxes only. This zip code is not included in this layer because the Truckee Post Office itself is zip 96161.

Demographics, population, housing, income, education, schools, and geography for ZIP Code 60064 (North Chicago, IL). Interactive charts load automatically as you scroll for improved performance.

This dataset contains all the code, notebooks, datasets used in the study conducted to measure the spatial accessibility of COVID-19 healthcare resources with a particular focus on Illinois, USA. Specifically, the dataset measures spatial access for people to hospitals and ICU beds in Illinois. The spatial accessibility is measured by the use of an enhanced two-step floating catchment area (E2FCA) method (Luo & Qi, 2009), which is an outcome of interactions between demands (i.e, # of potential patients; people) and supply (i.e., # of beds or physicians). The result is a map of spatial accessibility to hospital beds. It identifies which regions need more healthcare resources, such as the number of ICU beds and ventilators. This notebook serves as a guideline of which areas need more beds in the fight against COVID-19. ## What's Inside A quick explanation of the components of the zip file * COVID-19Acc.ipynb is a notebook for calculating spatial accessibility and COVID-19Acc.html is an export of the notebook as HTML. * Data contains all of the data necessary for calculations: * Chicago_Network.graphml/Illinois_Network.graphml are GraphML files of the OSMNX street networks for Chicago and Illinois respectively. * GridFile/ has hexagonal gridfiles for Chicago and Illinois * HospitalData/ has shapefiles for the hospitals in Chicago and Illinois * IL_zip_covid19/COVIDZip.json has JSON file which contains COVID cases by zip code from IDPH * PopData/ contains population data for Chicago and Illinois by census tract and zip code. * Result/ is where we write out the results of the spatial accessibility measures * SVI/contains data about the Social Vulnerability Index (SVI) * img/ contains some images and HTML maps of the hospitals (the notebook generates the maps) * README.md is the document you're currently reading! * requirements.txt is a list of Python packages necessary to use the notebook (besides Jupyter/IPython). You can install the packages with python3 -m pip install -r requirements.txt

Original Divvy Bikeshare Data obtained from here

City of Chicago Zip Code Boundary Data obtained from here

Tableau Dashboard Viz can be seen here

R code can be found here

Context

This is my first-ever project after recently completing the Google Data Analytics Certificate on Coursera.

The goal of the project are to answer the following questions: 1. How do annual riders and casual riders use Divvy bikeshare differently? 2. Why would casual riders buy annual memberships? 3. How can Divvy use digital media to influence casual riders to become members?

Casual riders are defined as those who do not have an annual membership, and instead use the service on a a pay-per-ride basis.

Content

Original Divvy Bikeshare Data obtained from here

The original datasets included the following columns: Ride ID # Rideable Type (electric, docked bike, classic) Started At Date/Time Ended At Date/Time Start Station Address Start Station ID End Station Address End Station ID Start Longitude Start Latitude End Longitude End Latitude Member Type (member, casual)

City of Chicago Zip Code Boundary Data obtained from here

The zip code boundary geospatial files were used to calculate the zip code of trip origin for each trip based on start longitude and start latitude.

Caveats and Assumptions

1. Divvy utilizes two types of bicycles: electric bicycles and classic bicycles. For the column labeled "rideable_type", three values existed: docked_bike, electric_bike, and classic. Docked_bike and classic were aggregated into the same category. Therefore, they are labeled as "other" on the visualization.

2. Negative ride lengths and ride lengths under 90 seconds in length were not included in the calculation of average ride length. -Negative ride lengths exist due to the end time and date being recorded as occurring BEFORE the start time and date on certain data entries. -Ride lengths 90 seconds and less were ruled out due to the possibility of bikes failing to dock properly or being checked out for a short time for maintenance checks. -This removed 90,842 records from the calculations for average ride length.

The process

R programming language was used for the following:

1. Create a new column for the zip code of each trip origin based on the start longitude and start latitude
2. Calculate the ride length in seconds for each trip
3. Remove unnecessary columns
4. Rename "electric_bike" to EL and "docked_bike" to DB

The R code I utilized is found here

Excel was used for the following:

1. Deletion of header rows for all dataset files except for the first file (April 2020)
2. Deletion of the geometry information to save file space

A .bat file utilizing DOS command line was utilized to merged all the cleaned CSV files into a single file.

Finally, the cleaned and merged dataset was connected to Tableau for analysis and visualization. A link to the the dashboard can be found here

Data Analysis Overview

Zip Code with highest quantity of trips: 60614 (615,010) Total Quantity of Zip Codes: 56 Trip Quantity of Top 9 Zip Codes: 60.35% (2,630,330) Trip Quantity of the Remaining 47 Zip Codes: 39.65% (1,728,281)

Total Quantity of Trips: 4,358,611 Quantity of Trips by Annual Members: 58.15% (2,534,718) Quantity of Trips by Casual Members: 41.85% (1,823,893)

Average Ride Length with Electric Bicycle: Annual Members: 13.8 minutes Casual Members: 22.3 minutes

Average Ride Length with Classic Bicycle: Annual Members: 16.8 minutes Casual Members: 49.7 minutes

Average Ride Length Overall: Annual Members: 16.2 minutes Casual Members: 44.2 minutes

Peak Day of the Week for Overall Trip Quantity: Annual Members: Saturday Casual Members: Saturday

Slowest Day of the Week for Overall Trip Quantity: Tuesday Annual Members: Sunday Casual Members: Tuesday

Peak Day of the Week for Electric Bikes: Saturday Annual Members: Saturday Casual Members: Saturday

Slowest Day of the Week for Electric Bikes: Tuesday Annual Members: Sunday Casual Members: Tuesday

Peak day of the Week for Classic Bikes: Saturday Ann...

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined because of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard Census Bureau geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous.