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- 1. Where Kinzie Corridor Workers Live by Census Tract, 2011 Workers Per Tract 1 - 9 10 - 18 19 - 35 36 - 57 Kinzie Industrial Corridor Source: U.S. Census Longitudinal Employer-Household Dynamics, 2011. http://onthemap.ces.census.gov/. PCS: NAD 1983 State Plane Illinois East. 0 2 41 Miles ¹
- 2. ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! Workers Aged 18 to 29, Per Tract 0 1 - 5 6 - 10 11 - 19 20 - 41 Workers Aged 30 to 54, Per Tract 0 1 - 5 6 - 10 11 - 20 21 - 41 Workers Aged 55 and Over, Per Tract 0 1 - 5 6 - 10 11 - 16 17 - 41 Kinzie Industrial Corridor ! Proposed BRT Stops Where Kinzie Corridor Workers Live, By Age and Census Tract, 2011 0 7 143.5 Miles ¹Source: U.S. Census Longitudinal Employer-Household Dynamics, 2011. http://onthemap.ces.census.gov/. PCS: NAD 1983 State Plane Illinois East.
- 3. ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! Workers Earning $1250/month or Less 0 1 - 5 6 - 10 11 - 20 21 - 41 Workers Earning $1251 to $3333/month 0 1 - 5 6 - 10 11 - 20 21 - 41 Workers Earning Over $3333/Month 0 1 - 5 6 - 10 11 - 20 21 - 41 Kinzie Industrial Corridor ! Proposed BRT Stops Where Kinzie Corridor Workers Live, by Income and Census Tract, 2011 0 7 143.5 Miles ¹Source: U.S. Census Longitudinal Employer-Household Dynamics, 2011. http://onthemap.ces.census.gov/. PCS: NAD 1983 State Plane Illinois East.
- 6. !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( !( LAKE POLK ARCHER CERMAK ROSCOE CHICAGO PERSHING DIVERSEY CORTLAND 91ST STREET 47TH STREET 79TH STREET 63RD STREET 69TH STREET 51ST STREET 74TH STREET IRVING PARK Proposed Ashland Avenue Bus Rapid Transit Stations Project Slated to Begin 2015 0 2 41 Miles ¹ !( Proposed BRT Stops Sources: Chicago Transit Authority: http://www.transitchicago.com/ashlandbrt/. Chicago Data Portal: https://data.cityofchicago.org/ PCS: NAD 1983 State Plane Illinois East
- 7. Ben Spies UPP 461 Wednesday FINAL PROJECT PAPER 1 Would Ashland Avenue Bus Rapid Transit Benefit Kinzie Corridor Workers? UPP 461 Final Project The advantage of GIS (Geographic Information Systems) lies in its ability to make complex problems more understandable through visual projection and spatial analysis. Some data are not clear when presented in a table or in a written format. When presented on a map, however, the data's relationship to the real-world environment becomes known, trends can appear, and in the case of data supporting public initiatives, mandates to action may become clear. Before I became familiar with GIS methodology, I had been searching for just this kind of direction regarding the Chicago Transit Authority's proposed Bus Rapid Transit (BRT) system for Ashland Avenue. I work for a nonprofit economic development agency and business association called the Industrial Council of Nearwest Chicago (ICNC). We work in the neighborhood known as the Kinzie Industrial Corridor on Chicago's Near West Side, an area of about one and a half square miles that is home to about 900 businesses and 16,000 primary jobs. In addition to a suite of free business development services, we offer our 400-plus member companies our advocacy on issues affecting their operations and their ability to remain located in Chicago. These issues may be as small as getting a pothole filled in or a no parking sign replaced, or as wide as regional land use and transit planning. One major proposal we have considered for several months is the aforementioned Ashland BRT. Our members have voiced serious concerns about BRT's potential to hinder truck traffic, and many have asked us to oppose the project. The counterargument from project advocates is that BRT service will benefit workers commuting to the Kinzie Corridor, where careful land use planning has created a major employment center with very little nearby residential. ICNC has gotten conflicting anecdotal reports from our members as to where the Kinzie workforce actually does live: some believe the majority of Kinzie commuters have clustered south and west in the Orange Line corridor, while others think that as businesses and employees begin to skew younger our workforce base is moving to “hip” northwest neighborhoods like Logan Square. Jobs in the Kinzie Corridor have become an interesting mix of blue- collar industrial and high-tech, innovative design; as such, the questions of where our workforce lives, and therefore what kind of transit plans would serve them best, have become important. The potential for GIS to explore these questions was immediately apparent to me. For this project, I sought to answer the dual questions of: 1) “Where do commuters to the Kinzie Corridor live?” (looking at City of Chicago only); and 2) “Would Ashland BRT serve a
- 8. Ben Spies UPP 461 Wednesday FINAL PROJECT PAPER 2 significant portion (ten percent or more) of these commuters?” I built my hypothesis on the second question: I believed a less-than-significant (ten percent or less) portion of workers in the Kinzie Corridor would be able to directly benefit from Ashland BRT in their work commutes. I would employ geospatial analysis of the commuters' home locations in relation to proposed BRT service to explore my hypothesis. First, I would map the concentrations of Kinzie commuter residences across Chicago to the most accurate geographies possible. Next, I would map the proposed BRT stations on Ashland. Finally, I would create a buffer of walking-access around each station, and use GIS tools to select geographies within that buffer and add up the total of Kinzie workers within that proximity. Thankfully, the raw data I sought to answer these questions was indeed available, but it would take careful implementation of GIS tools to arrive at my conclusion. My first objective was acquiring the mappable information I would need to complete the analysis: my “smart geography” and “dumb data” (see appendix). Virtually all of my “smart geography” came from the Chicago Data Portal (https://data.cityofchicago.org/). To geographically illustrate the concepts I would be exploring, I downloaded a polygon shapefile of Chicago's 2010 Census Tracts, taken directly from U.S. Census TIGER, another polygon of the city's Industrial Corridors, a point-data shapefile of all existing CTA bus stops, a line-data shapefile of existing CTA rail lines, and a point-data shapefile of existing CTA rail stops. In order to illustrate the proposed BRT stops, I would have to do some selection work on the shapefile of existing bus stops, which I will cover later. My “dumb data” would be the actual information on the number of Kinzie Corridor commuters who live in various geographies throughout the city. The challenge would be defining only those people who work in the Kinzie Corridor area, but examining where they live city-wide. At first, I was unsure if a resource for finding information this specific would be available, but after some searching I made an incredible find: the U.S. Census's Longitudinal Employer-Household Dynamics (LEHD) “On the Map” explorer (http://onthemap.ces.census.gov/). This fantastic tool collects and displays data on the relationship between where people work and where they live. LEHD is administered by the U.S. Census, which aggregates data from all fifty states taken from Unemployment Insurance earnings, the Quarterly Census on Employment and Wages, and the decennial Census itself. With these robust sources, LEHD has information on where all people both work and live, at least in most states, and can visualize the relationships between them. That is, a user can define a geography (for instance a city, census tract, or a custom geography by drawing a shape or uploading a shapefile), and define this as
- 9. Ben Spies UPP 461 Wednesday FINAL PROJECT PAPER 3 either the “work” or the “live” location. The user can then get a visualization of the of the opposite definition, for instance where all of the people who work in the selected geography live. This data is accurate to the census tract level. I pulled my data by first defining a “work” geography of the Kinzie Corridor itself: I uploaded the Industrial Corridors shapefile, taken from the Chicago Data Portal, directly to the LEHD On the Map interface. I then clicked the Kinzie Corridor polygon on the map to define only that particular industrial corridor as my geography of interest. I set my analysis options to recognize that this was a “work” geography, and to look at “destinations” of workers in this geography to find their home locations. I chose “primary jobs” only, not wishing to count second and third jobs whose multiple commute variables might skew my results. I chose the most accurate geography result possible, which was census tracts. In an ideal world, the results would be accurate to at least the census block level, to get the best readout on the households that would actually be within walking distance of BRT, but this was not possible. Likewise with the data vintage: I chose to use 2011 data, the most recent available. On the Map returned 2970 total census tracts in which Kinzie commuters live, stretching from southwest Wisconsin to well into Indiana, and 16,026 total primary jobs (or “commuters”). I noted that for my analysis I would only be interested in those census tracts located within the City of Chicago, on the assumption that anyone commuting from outside the city would not be taking CTA. With my data properly selected, I exported it as a CSV file I called HomeTracts. I was fortunate in that I had few problems wrangling my data, though I did have to clean up the column aliases in HomeTracts for compatibility with ArcGIS, and format the census tract ID column for text rather than numbers. The first goal was to create a map that answered my first question: Where does the Kinzie workforce live? I knew that the GIS could create a map showing this distribution visually, which would be useful both for discussion on BRT and for ICNC's own gratification. I would have to join my HomeTracts “dumb data” with the census tract shapefile “smart geography”, which I did based on the census tract ID column that both files had in common. Again, the only real difficulty with the data that I had was formatting both columns for text, which I fixed easily enough. I found that, symbolizing on Jenks method after I completed my join, the default five classification levels were a bit too graded and difficult to read. I reduced this to four classifications, and the results became clearer: though Kinzie does seem to draw a number of commuters from a narrow swath on the southwest side, the main employment base seems to reside on the North Side of the city west of the Chicago River (see map one, “Where Kinzie Corridor Workers Live by Census Tract, 2011”). A significant portion also seems to
- 10. Ben Spies UPP 461 Wednesday FINAL PROJECT PAPER 4 live in the tracts surrounding the east end of the Kinzie Corridor, in the West Town/West Loop areas. One surprising finding was that the Kinzie Corridor does not seem to draw many workers from the South Side of the city at all, mostly just between one and nine commuters per tract. The data contained in this map will have a direct bearing on our BRT analysis results, but is also important to ICNC on its own. I next broke this analysis down by worker age and income, creating six distinct maps that illustrate living patterns for workers in three different age categories, and three different income levels (see maps two and three, “Where Kinzie Corridor Workers Live, by Age and Census Tract” and “By Income and Census Tract”). While these maps on their own may illustrate some trends regarding work-live patterns as related to these demographic indicators, they would require more in-depth geospatial analysis to draw meaningful conclusions, like how commute distance relates to income level, which falls outside the scope of this project. To illustrate the proposed BRT station locations, I started with the point-data shapefile of existing CTA bus stops and wrote a complicated SQL query under “Select by Attributes”, which included the name of each cross street to house a proposed BRT stop. This information was taken from the Ashland BRT marketing materials on the CTA's website (http://www.transitchicago.com/ashlandbrt/). With those in place as a separate layer, I created a half- mile buffer around each station, dissolved into one polygon, under the assumption that a half-mile would be an acceptable distance for commuters to walk to transit access (see map four, “Tracts Within Walking Distance of Ashland BRT”). To actually pull data on the number of commuters affected, I ran a “Select by Location” on my census tracts file, defining the selection on tracts that had their centroid within the buffer. This method, though as accurate as possible given the circumstances, illustrates one shortcoming of the LEHD data: because it was only accurate to the census tract level, and because my buffer cuts through several tracts, I would necessarily have parts of some tracts that were within my buffer but not included in the selection, and would likewise have some parts of tracts that were included in the selection but outside of the buffer. I had to assume that these inaccuracies would balance each other out, and give us a roughly accurate total, at least for discussion purposes. With my selection in place, I ran statistics on the selected records in my tract attribute table, arriving at 1031 commuters in my selected tracts (or roughly within the half-mile buffer), out of a total of 16,026 commuters. This comes to an effective rate of 6.43% of Kinzie commuters living within walking distance of Ashland BRT, which appeared to confirm my hypothesis. I did realize, however, that this analysis on its own was perhaps a bit myopic. BRT might not
- 11. Ben Spies UPP 461 Wednesday FINAL PROJECT PAPER 5 only be useful to commuters living within walking distance of BRT itself, but to commuters living near rapid transit which might transfer to BRT. I would have to expand my analysis to include transfers from other transit modes. I decided to limit this to CTA rail connections, accepting that it would not be plausible to analyze all possible mode split options that could feed into a BRT commute. I made a few more assumptions: 1) commuters would transfer from rail to BRT, but not from bus to BRT; 2) commuters would only tolerate a single transfer; and 3) commuters would not travel away from their ultimate destination (the Kinzie Corridor) in order to make a BRT transfer. These assumptions, though perhaps generous, were necessary in order to scale my analysis of BRT effectiveness to something that might be useful. If I included every possible mode split, no matter how implausible, we would end up with an unrealistic idea of BRT's effectiveness. I added a point-data shapefile of the CTA's rail stations, and manually deselected those stations which were not on a rail leg that could reasonably be expected to connect to Ashland BRT on a trip to the Kinzie Corridor. I also removed stops on lines that did not connect at all, and those which traveled to the Kinzie Corridor directly thus negating Ashland BRT, like the Harlem branch of the Green Line. I then created a half-mile buffer around the remaining stations, and executed a union to combine this buffer with my existing BRT stations buffer. With one large, non-contiguous walkability buffer in place, I ran another Select By Location on the census tracts with centroids within the shape (subject to the same limitations as the last analysis). Again running statistics on the selected attributes in the table, I found 2247 commuters living in tracts within walking distance of BRT or a rail station on a line which might transfer to BRT towards the Kinzie Corridor (see map five, “Tracts Within Walking Distance of Ashland BRT”). This amounts to 14.02% of Kinzie commuters, nullifying my hypothesis. Spatial analysis this complicated could only be done with GIS. Distance cannot be drawn on an Excel table; residential clustering patterns cannot be clearly defined without a map. In working with data that is tethered to geography, to answer questions related to location and distance, GIS proved to be a reliable tool not only to accrue new data, but to visualize it for the purposes of discussion. Indeed, ICNC now has some useful data as to BRT's efficacy to the neighborhood workforce's commute, which we will discuss as we continue to cultivate our position on the project. Given the robustness of the LEHD data, we could even break this out further to analyze BRT's efficacy based on worker age or income, as shown on the supplemental maps. While we are not ready to draw hard and fast conclusions on BRT, having concrete, reliable data on how it could serve our workforce, thanks to GIS, puts us in a much better position to make decisions.
- 12. Ben Spies UPP 461 Wednesday Sources 4/21/14 through 4/28/14 ArcGIS Ver. 10.1, Build 3035 Windows 7 Operating System Data Sources 1. U.S. Census Longitudinal Employer-Household Dynamics “On The Map”. Online interface with data for export on where workers live and work. Collected from U.S. Census, the Quarterly Census on Employment and Wages, and Unemployment Insurance reports. Available http://onthemap.ces.census.gov/. Retrieved 4/21/14. 2. Chicago Data Portal. Searchable online database containing, among many other things, shapefiles on geographies and geographic features within the City of Chicago, including census tracts, transit amenities, and industrial corridors. Available https://data.cityofchicago.org/. Retrieved 4/21/14.