Cultural & Community Events and Gentrification

Cultural & Community Events and Gentrification: Observing the Relationship Between Event Permits and Income

by Census Tract in Chicago from 2012 to 2017

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Maggie Schafer and Evin Vinson UPP 461 Final UIC College of Urban Planning and Policy 1

Context & Research Question Chicago is known for its community & cultural events; its street fairs, block parties, and music festivals are a mainstay of its landscape. Although these events are thought to build community and promote economic growth, we question whether they play a role in gentrification and the associated displacement of low-income residents.

The City is actively trying to entice the creative class to relocate, while at the same time doing little to protect its long-time residents from gentrification. The creative class represents a modern phenomenon of knowledge workers with lucrative professions who are attracted to urban environments for their culture, affordability, and walkability. But as higher income individuals move into lower income neighborhoods, property values and rents climb until the low-income families are forced to move.

While some events are open to low-income residents, some argue the creative class is their key demographic. This leads us to the question: what is the relationship between community & cultural events and income in Chicago?

More specifically we explore: • • •

Do community & cultural events occur more frequently in higher income areas?

Are new events more likely to occur in areas with rising incomes?

Do areas with increasing incomes host more events than those with decreasing incomes?

These questions are important because community & cultural events are often presented to low-income communities as opportunities to bring economic and social benefits to local businesses and residents. However, benefits must be balanced with the potential to open the door to gentrification; this report aims to provide data to stakeholders to help them better understand that balance.

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Definitions To narrow our scope of research, we defined major terms, geographic boundaries, and time range.

• Gentrification is defined in terms of change in median household income over time.

• Community & Cultural Events is defined as events with over 200 people occurring on public property, including festivals, parades, corporate events, picnics, and markets.

• Geography is set as the City of Chicago on the census tract level.

• Time Range is set between 2012 to 2019. This is the largest time range event data is available for.

Methodology Our methodology can be broadly divided into three sections: (1) tracking income data, (2) tracking & geocoding events data, (3) combining & evaluating data. Steps within each section are explored below.

(1) Tracking Income Data a. Collecting Data We downloaded American Community Survey (ACS) data on median household income by census tract for 2012 and 2019, as well as shapefiles with Cook County census tracts. b. Cleaning Data We deleted unnecessary data, brought cleaned data into ArcMap, and converted it to a Dbase. We projected and joined the census tract shapefile with income data using census tract number as a hook.

We then clipped the data to only show Chicago. c. Symbolizing Data We symbolized each census tract by income level, categorizing them by manual breaks to divide incomes levels based on their proportion of the Cook County median income, as shown in Figure 1.

More information on steps involved in (1)Tracking Income Data is available in Appendix II – Project Log.

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(2) Tracking & Geocoding Events Data a. Collected Data We found data on events through two City of Chicago Data Portal sources: the Chicago Park District Events Permits and the Chicago Department of Transportation Right of Way Permits. Categorization for these datasets differed, so understanding how to filter for relevant events in each was necessary.

While Park District data included the “park number” of parks in which events took place, it didn’t include addresses; it had to be matched with another dataset that contained both park numbers and addresses to identify the latter. b. Combined & Cleaned Data from Two Sources We cleaned both datasets to include only relevant matching columns, combined them, and sorted out data from 2012 and 2019. Rows representing multiple permits for the same event were deleted; this took numbers down from approximately 3500 annually to a 1000. c. Geocoded Events We used a shapefile with Chicago Streets to geocode events in 2012 and 2019.

More information on steps involved in (1) Tracking & Geocoding Events Data is available in Appendix II – Project Log. (3) Combining & Evaluating Data a. Combined Shapefiles We combined shapefiles with geocoded events and symbolized median household income by census tract for 2012 and 2019 to create Figure 3 and 4. b. Explored Events Hotspots We ran hotspots analyses for 2012 and 2019 events and compared them with a map that highlighted only high- and very-high income census tracts.

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c. Tracked Changes in Income After completing a table join between median household income by census tract for 2012 and 2019, we created a new data field that formulated the difference between income for each census tract over the years. This was symbolized using manual breaks to show tracts where income increased or decreased when accounting for inflation. d. Overlaid New Hotspots with Changes in Income We identified “new” hotspots that occurred in 2019 but not 2012 by conducting a reverse clip on event hotspot analyses for those years. We clipped shapefile created in step 3(d) with the new hotspot analyses to investigate whether new event hotspots were more likely to take place in areas with increasing incomes e. Compared Number of Events in Census Tract with Changes in Income We identified census tract areas with both significant increases and decreases in income from 2012 to 2019. We created clips with just these census area tracts and the geocoded events they intersected with.

Technical Challenges and Data Issues We had issues sharing maps and datasets between team members; attempts were met with error messages, even after appropriate troubleshooting. We were also limited by data availability. Sources changed categorizations frequently; for example, the way farmer’s markets were categorized changed over time and source, so they were unable to be specifically sorted for.

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Findings & Key Maps The following maps and findings provide insights into our research questions.

Do community & cultural events occur more frequently in higher income areas?

Figure 3 and Figure 4 show median household income by census tract. We see a high concentration of events in high-income areas such as the Loop and Lincoln Park side for both 2012 and 2019. In contrast, we see fewer events on the lower income areas on the South and West sides. The overlap between the census tracts with high median household incomes and the presence of many events is further illustrated on the following page with Figures 5-8. The figures on the left show event “hotspots”, which illustrate where events cluster spatially, while the figures on the right highlight census tracts with either high or very high incomes.

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Are new events more likely to occur in areas with rising incomes?

Figure 9 shows census tracts where median household income increased significantly from 2012 to 2019, as well as those that had a decrease in median income when accounting for inflation. We then clipped this changein-income shapefile with a shapefile of the locations of event hotspots that occurred in 2019 but not 2012 (Figure 10).

Observing the results (Figure 11), we do not see a predominance of areas with rising incomes associated with new events.

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Do areas with increasing incomes host more events than those with decreasing incomes?

We compared changes in number of events occurring in two census tract areas – Logan Square and South Lawndale – with changes occurring in Chicago as a whole.

Logan Square experienced an increase in median household income between 2012 and 2019; South Lawndale experienced a decrease in median household income during that time. Figures 12-15 show events and median household income by census tract for both areas in 2019 and 2020, and Table 1 breaks down the data from these Figures. All areas experienced a decrease in number of events over this time period; additionallly, in both time periods South Lawndale had more events than Logan Square. While this sample size is extremely small, comparing these two census tract areas does not show that areas with increasing incomes host more events than those with decreasing incomes.

Area Logan Square South Lawndale Chicago (Total)

No. of Events, 2012 11 15 1075

No. of Events 2019 9 12 895

% Change in Events -22.2 -25 -20

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Limitations and Future Work Our largest limitation came from data availability. Event data was unavailable before 2012. We hypothesize that tracking events past 2012 would show larger shifts in the locations of events. Another limitation was the inability to relate the events data with the income data because the events were coded with addresses while income was coded coded by census tract. Tracking number of events that fall within each census tract would have allowed to conduct more thorough quantitative research.

In addition to identifying ways to track number of events by census tract, future studies should explore the relationship between income and other elements of gentrification, including race, education, and housing prices. Additionally, future work should explore how population plays into these relationships.

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Appendix I: Data Sources American Community Survey 1-Year Estimates: U.S Census Illinois Shapefile by Census Tract: U.S. Census Chicago Park District Event Permits: City of Chicago Data Portal Chicago Department of Transportation Right of Way Permits: City of Chicago Data Portal Chicago Street Lines Shapefile: City of Chicago Data Portal

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Appendix II: Project Log a.

Income Tracking by Census Tract 1. Steps ○ Download Illinois shapefile by county.

Projections ○ Open ArcCatalog → Preview the 2012 Illinois by census tract Shapefile to verify if the shape is being properly projected by opening properties and going to XY Coordinate System ○ If the file is not being project correctly go to ArcToolbx →Data Management Tools → Projections and Transformations → Project ○ In the Project window, drag the Illinois shape file into the Input Data or Feature Class box → in Output Coordinate System select Projected Coordinate Systems → State Plane → NAD 1983 (2011) (US Feet) → NAD_1983_2011_StatePlane_Illinois_East_FIPS_1201_Ft_US → Select OK → Select OK ○ Repeat these steps with a Chicago by census tract Shapefile DBASE ○ Download Income data from the US Census website (2012/2019) → Open the file in Excel → remove unnecessary data leaving only Median household income by census tract → isolate the last 11 digit of the GEOID data in its own column.

■ To isolate the codes add a blank column next to the ID column. In this column, type the equation “=right(A2 [this is corresponding cell for GEO_ID], 11 [the last eleven characters in the corresponding cell).

■ Use the action button to drag to the bottom of the spreadsheet to calculate all the corresponding GEO_ID’s ■ Title the new column “GEO_ID”. (titles must be under 10 characters, and cannot contain spaces and special characters) ■ Widen columns to show all information presented. Any information that is not visible will not show up in GIS ▪ Make sure all numbers are aligned to the right, and all text is aligned to the left. ○ Open Arcmap and drag the 2012/2019 csv file onto the canvas → Right click on the 2012/2019 file in the Table of Contents → go to data → go to Export ○ In the Export Data screen, click the file button under Output Table → Rename the file and save as a DBase Table Joining Tables 13

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b. .

Open the attribute table ■ The Total_pop field is misaligned and must be corrected ■ Click “Table Options” and select “Add Field” ■ Title new field “GeoID” and select it as “Double” to correct misalignment Click “Table Options” and select “Join and Relate” → Select “Join” → In the first box select “GEOID” and relate that to “Geo_ID” Export the joined table Symbology Right click on the new shapefile that is created → got to Properties → Symbology → Quantities → In Values select Income Level → select 6 classes → click OK.

Events by Census Tract 1.

Sought out data from City of Chicago Data Portal. The following was done to organize events from each source: a. Chicago Park District Event Permits ■ Researched Park District event permit categorization to understand what events should be included.

Categorization methods changed over the years, making it essential to understand the annual nuances.

■ Based on categorization, filtered out events with under 200 people.

■ Filtered out athletic events, as the majority of these are running races that span many parts of the City.

■ The park location of each event originally did not have addresses. To identify addresses we used the Chicago Park District Address list; both this excel sheet and the event permits excel sheet contained a park number. We were able to match by converting both excel files to csv files, importing them into arcmap, converting them to dbase tables and performing a table join based on the park number. Once the tables were joined, the layer was saved; in ArcCatalog it was converted back to an excel file to be combined and sorted with other data sources. b. Chicago Department of Transportation Public Right of Way Permits ■ Looked at Right of Way Permits and sorted for the following events: festivals, parades, corporate events, picnics, sidesales. Sorted out events such as general road closures, and athletic events.

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2. Combined Data from two sources in an excel spreadsheet: a. Cleaned spreadsheets so as to include relevant matching columns in each (Event name, event date, event categorization, event address). b. Next to the event date column, added a new column next to it to pull just the year. Filtered out for the year 2012; then filtered out for the year 2019. c. Many events featured multiple permits, for example, a street fest would need separate permits for different stretches of the street.

So we deleted events with the same name that repeated on the same and/or consecutive dates in the same area. This took our annual number of events down from about 3500 to about 1000.

2012 event file and 2019 event file saved as 2012_events.csv and 2019_events.csv, respectively.

3. Events were geocoded through the following steps: a. Opened new arcmap doc b. Found a shapefile with Chicago Streets (CenterLine_.shp) c. Once address locator was created, went to properties>>Geocoding options and changed “Match with no zones” to “Yes” as we did not have all of the zip codes for addresses. d. Right clicked on folder in TOC>>new>>address locator ■ Address Locator Data: Dual Range ■ Reference Data: CenterLine_.ship ■ Saved output as Chi_Address_Locator e. Once address locator was created, went to properties>>Geocoding options and changed “Match with no zones” to “Yes” as we did not have all of the zip codes for addresses f. 2012_events.csv pulled in and converted to dbase (2012_events.dbf) g. Right clicked 2012_events.dbf.>>Geocode Addresses ■ Choose Chi_Address_Locator as Address Locator ■ Matched based on “Event Address” column ■ Went through and matched unmatched addresses ■ Saved layer as 2012_events_geocoded Repeated steps f. And g. For 2019_events

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