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1. Introduction..........................................................................................Page 1 2. Geographic Boundaries.................................................................Page 2 3. Demographic Summary................................................................Page 3 4. Metro Area’s Existing Rail Systems..........................................Page 4 5. Methodology........................................................................................Page 5 6. Extracting Data from Buffers......................................................Page 6 7. Distance Assessment.......................................................................Page 10 8. Hot Spot Analysis..............................................................................Page 11 9. Infrastructural Conditions Assessment..................................Page 12 10. Conclusions...........................................................................................Page 13 11. References.............................................................................................Page 14

INTRODUCTION - Disability & Transit In the U.S.!



3 !

Since the passage of the Americans with Disabilities Act in 1990, it is mandatory for all transit agencies to provide fully accessible

facilities and services.


There has been a push

toward transitioning users with disabilities from

costly paratransit services toward fixed-route and rail services.

Despite both these legal measures and efforts from transit agencies, barriers

to using public transport still exist for persons with disabilities.

Introduction: Passed in July of 1990, the Americans with Disabilities Act (ADA) made legislative history in that it prohibited discrimination based on disability in areas such as employment, public services, telecommunications and transportation (Rosenbloom, 2007). In the years that followed, public transit agencies across the country have since sought to improve basic transit operations, enhance existing services, and when necessary, provide alternate services for better meeting the needs of customers with disabilities. Traditionally, transit agencies have provided these services in two ways: fixed-schedule services, operating on a predetermined route and paratransit or “dial-a-ride” services, operating on a demandresponsive system (Koppa, 1998). Despite both alternatives being ADA compliant, and meeting all the necessary legal requirements and obligations to provide fully accessible services, today, nearly twenty-five years after the passage of the ADA, barriers to public transport continue to exist for persons with disabilities and oftentimes prevent them from utilizing those services.

Infrastructure barriers such as lack of curb cuts or cracked sidewalks continue to block access to transit stations or stops, and long distances to nearby stations oftentimes prevent disabled or elderly users from even considering using transit. While addressing these matters might seem as if it should be considerably straightforward, the issue is only complicated further when considering that in most communities, multiple agencies are responsible for different portions of a pathway. In a time of economic turmoil and ever-tightening budgets, municipalities seem all too likely to neglect accessibility responsibilities or pass them onto others. This project attempts to explore a more systematic approach to addressing this large and complicated issue by exploring public transportation and accessibility specific to the greater Los Angeles area and attempts to answer the question of whether existing fixed-route and rail Metro systems are meeting the needs of Los Angeles’ disabled population. Specifically, I do this by performing a spatial analysis of population densities of the disabled and the elderly (or people that might night drive themselves and could benefit from public transit) and compare that to existing transit systems.

“...nearly twenty-five years after the passage of the ADA, barriers to public transport continue to exist for persons with disabilities”


Infographic, Brief Overview of Disability and Transit in the United States. (Icons drawn using Adobe Illustrator.) PAGE 1

L.A. METRO Boundaries & County Service Planning Areas

Geographic Boundaries: Prior to performing spatial analysis, I began this project by determining my geographic boundaries, or my area of study. Specifically, I knew I’d be studying all of Los Angeles County for much of the demographic comparisons, but when assessing transit availability, I felt it important to concentrate on a smaller area within the County that met the following criteria: 1. Was within the Los Angeles County Metropolitan Transportation Authority’s transportation network. 2. Had a population density comparable to that of the rest of the County and preferably had a decent amount of transit users. Map By: Carla Salehian Source: L.A. County GIS Shapefile Library


In setting this criteria, I wanted to ensure I had a population density representative of the greater county region with a significant amount of transit riders whose livelihood could be impacted (for better or worse) as a result of changes in transit availability. Put simply, I wanted my analysis to be significant for the existing population. After seeing a map of the County subdivided into

8 “Service Planning Areas” including (San Fernando, San Gabriel, West Los Angeles, East Los Angeles, South Los Angeles, etc.) I was immediately drawn to the “Metro” area centrally located within the County and considering it contained Downtown and its peripheral areas, thought it would most certainly match the established criteria. Figure 2 details the geographic boundaries in more detail and is meant to orient the reader as to where this area is within the greater County region. First, the inset map displays the aforementioned Service Planning Areas (a shapefile of which was found in the “LA County GIS Shapefile Library”) and the Metro boundary is outlined in yellow. The larger map displays the Metro service area in greater detail, this time, with major freeways displayed and labeled in order to further orient the reader. The major freeways were selected amongst a larger shapefile with an “attribute sub-sets selection” (Major Freeways = 405 or 5 or 10 or 110 or 2 or 101). Here, it becomes clear that the Metro boundary includes the Downtown area and much of the Hollywood area to the northwest. Additionally, being that the project would be working with a considerable amount of census data,

the map also displays the boundaries of the 320 census tracts that make up the Metro area. In order to display this, a shapefile of all the Los Angeles county census tracts was added and then clipped to feature only the Metro census tracts. Both the shapefiles for the major freeways and the Los Angeles County census tracts were drawn from Mapshare, UCLA’s spatial data repository.


Map, Boundaries and County Service Planning Areas. (Sources: LA County GIS Shapefile Library, UCLA Mapshare) PAGE 2

L.A. METRO Demographic Summary/Midterm Review!

Demographic Summary:




DISABILITY INDEX Maps By: Carla Salehian , Source: 2010 U.S. Census


After setting the geographic boundaries, I performed a basic demographic overview of the Metro area with regard to topics specific to transportation and persons with disabilities and then compared each of these topics to the LA County region in order to better understand the spatial and societal characteristics of my area of study. Specifically, I gathered 2010 census data and created tables with information on total population statistics, method of transportation to work, the elderly population, and the disabled population. In terms of population, the Metro service planning area has a total population of 1,116,508 with a mean population density of 3,489 per tract, which was found to be comparable with the rest of the county. When studying the mode of transport to work (for the working population, 16+), not surprisingly, the vast majority of the population in both the LA Metro area and county drive to work but when looking specifically at the percent of the populations that use public transit, the metro area was over double that of the County. 18% of the population of the Metro area uses public transport

for their commute while only 7% of the County does. The displayed “Transit to Work” map in Figure 3 gives a spatial representation of this data by showing the areas in which public transportation usage is highest at both the Metro and County areas. In order to create the “Public Transit” category, I used the aggregate attribute fields skill by combining the census categories “Bus/ Trolley Bus,” “Street Car/Trolley Car,” “Subway/Elevated,” and “Railroad.” As expected, the highest concentrations are west of the Downtown area. Senior population density was also explored for this study and it was found that just over 10% of the Metro population was either 65 years or older (10.6%) with a slightly higher County average of 11.3%. Again, the “Age 65+” map in Figure 3 gives a spatial representation of this data by showing the areas with the higher concentrations of elderly individuals in a darker green. Lastly, physical disability was, of course, another major category to explore for this study but unlike the previous categories, data on disability had to be drawn from rather than from the U.S. Census directly. There, I found statistics at the census tract level describing the number of individuals who could not perform certain physical tasks. I took this information to create a “Disability

Index” that I would use throughout the rest of the project. Specifically, for each census tract I added the number of individuals who cannot: “perform any physical activity” + “walk quarter mile” + “climb ten steps” + “stand two hours” + “bend, stoop, or kneel” and calculated the average for each. In the end, what resulted was the “Disability Index” map also displayed in Figure 3 that shows the areas with the highest concentrations of disabled individuals as calculated by the index.


Map, Demographic Summary/Midterm Review. (Sources: 2010 U.S. Census and PAGE 3

L.A. METRO Existing Rail Lines and Stations!

Metro Area’s Existing Rail Systems: QUICK FACTS: 16 stations! 155,940 riders/week! !

21 stations! 41,987 riders/week! !

22 stations! 92,120 riders/week! !

12 stations! 20,656 riders/week!

Map By: Carla Salehian Source: LA Metro


After gaining a better understanding of the demographic conditions of the Metro area, my next step was to observe the existing transit infrastructure in the area. As mentioned before, the primary focus of this project is on the L.A. Metropolitan Transportation Authority’s rail lines and stations, and as a result, my next steps were to further research and explore the existing rail lines and stations within the Metro area Boundary. The map in Figure 4 features the now familiar Metro Area boundary in relation to other service planning areas in addition to its 320 census tracts. Layered on top of this are a series of shapefiles (pulled from LA Metro’s website) illustrating each of the major rail lines and stations that intersect the Metro area and all connect to the central Downtown area of Los Angeles. The Red/ Purple lines include a total of 16 stations that span west into the Koreatown area and northwest through Hollywood. Further research on L.A. Metro’s website found that of all the rail lines throughout Los Angeles, the Red/Purple line was travelled on the most (with an average of nearly 156,000 riders/week). As for

the other lines, the Gold line connects central Los Angeles with the eastern region and features 21 stations in total (with 41,987 riders/week), the Blue Line connects central Los Angeles with the southern portion of the County and includes 22 stations (and a little over 92,000 riders/week), and lastly, the Expo Line is comprised of 12 stations (with over 20,000 riders/week.) In terms of consolidating information displayed on the map to reduce clutter and redundancy, the legend features only the station names (it was assumed that the correlated lines would be implied.)


Map, Existing Rail Lines and Stations. (Sources: LA Metro)


PROJECT OUTLINE - Research Questions! 1. 2. 3. 4.

Are there demographic discrepancies between the transit rail stations and the studied area at-large?! ! ! What is the average distance to a station?! ! ! Are current station locations meeting the needs of users with disabilities? Where should future stations be located?! ! ! Which stations might have infrastructure conditions in most need of refurbishment?!


Methodology: Research Questions Equipped with a much better understanding of the existing conditions and rail-transit infrastructure in the area of study, my next task was to start considering the ways in which I would tie this information to my broader topic of study: answering the question of whether existing rail Metro systems are meeting the needs of Los Angeles’ disabled population. In order to do this, I first broke down the topic by creating a series of more specific research questions (as seen in Figure 5.) The first of the research questions asks whether there are demographic discrepancies between the transit rail station areas and the studied Metro area at-large. This question is posed to begin to determine whether the transit station locations are representative of the entire area at large. I would answer this question through a series of maps in which I extract data from a buffer and compare that to the data for the entire area. The second research question is a bit more straightforward in that it simply asks what the average distance to each station is. Knowing distance was a major factor in preventing disabled users from

considering transit, I felt this information would be particularly valuable in determining whether existing sites were meeting the needs of LA’s disabled population – which is the third research question I listed in the figure. I would answer this question and the question of where future stations be located through spatial analyst/hot spot analysis tools. Lastly, being that this study also takes into consideration the infrastructural barriers to accessible transit, I pose the question as to which stations might have infrastructure conditions in most need of refurbishment. The set of maps that follow this section of the report answer each of these questions in this order. Similar to the figures that preceded this section, all maps were created entirely using ArcMap 10 with formatting adjustments to the titles, legends, and supplementary facts done on Adobe Illustrator.


List of Proposed Research Questions


METRO STATION BUFFERS VS. AREA Transit to Work Population Density!

Extracting Data from Buffers As mentioned before, the first set of maps I will be describing were created in response to the first of the four research questions listed in the methods section of this report. In attempting to learn whether there are demographic discrepancies between the transit rail stations and the studied area at-large, I used the extract statistics from a buffer tool. Specifically, I wanted to create halfmile radius buffers around each of the stations within the Metro area boundary and would then display certain data within the buffer. I’d also create an inset map to display data for the entire Metro area.




In order to create these maps, I used a GIS Model Builder as a means of automating the multi-step process. This particular model (pictured below) was used to join the Metro census tracts with the data tables I’d compiled, to create the aforementioned half-mile buffers around each rail station, and lastly, found the census tracts that intersect the buffers in order to display the data within the new intersect boundaries.

METRO STATION BUFFERS VS. AREA Transit to Work Population Density The first buffer map (Figure 6) compares percent of working population that uses public transit to get to work within a half-mile buffer compared to that of the Metro area, at-large. Being that this study focuses on public transportation

Map By: Carla Salehian Source: 2010 U.S. Census, LA Metro


Map, Metro Station Buffers vs. Area - Transit to Work Population Density. (Sources: LA Metro, 2010 U.S. Census) Extract Data from Buffer Model




and facilitating transit accessibility for the disabled, it was important to first gain a better understanding of the existing transit usership in the area. As one might expect, the mean density was higher for the census tracts intersecting the halfmile buffer, but the differences weren’t all that significant when compared to the entire Metro area; just over 20% of the working population within the halfmile buffer uses public transport for their commute as compared to 18.5% of the working population in all of Metro. Also interesting to note was that transit usage in the cluster of tracts around the downtown area (including Union Station) had lower percentages than other areas in the periphery.




$30,732 METRO AREA

Median Income Levels

Map By: Carla Salehian Source: 2010 U.S. Census, LA Metro


The next sets of buffer maps illustrate data more specific to transit accessibility. First off, considering income plays a major role in transit ridership for all users, I felt it would be especially important to consider for disabled users; particularly because lower-income disabled users might have less alternative transportation options available to them other than transit. When applying the variable of “Median Household Income” to the entire Metro

area (see inset), I noticed an interesting spatial distribution pattern. According to 2010 census data, the downtown area had the highest concentration of lower income households (within the $0 - $20,000) range. Surrounding this core area, average incomes progressively increase (as evidenced by the darker blue tones until one reaches to outer northwestern periphery from downtown where you see much higher median income levels (within the $60,0001 $145,341) range. Needless to say, it is clearly evident that the Metro service planning area has a very broad range of income types within its boundaries. The overall average of the median household incomes reported for each of the census tracts within the entire Metro area was about $45,000. In comparison, the census tracts intersecting the half-mile buffer were also varied in income bracket types. The highest concentration of lower-income range populations was around the downtown area while the majority of the station locations seemed to be situated in areas with a median household income range between $20,001 - $35,000. In total, the overall average of the median household incomes for populations in the half-mile buffer was around $30,700 which was considerably lower than that of the entire Metro area.


Map, Metro Station Buffers vs. Area Median Income Levels (Sources: LA Metro, 2010 U.S. Census) PAGE 7

METRO STATION BUFFERS VS. AREA - Senior Population Density



10.6% Map By: Carla Salehian Source: 2010 U.S. Census, LA Metro


The third buffer map displays information with regard to senior population (or population percentage of those 65 years old or above). In both the inset and larger map seen in Figure 8, the darker green tones represent higher concentrations of senior population per census tract. As demonstrated by the inset map, the overall the senior population concentrations within these tracts seem a lot more dispersed (especially in comparison to the income and transit user maps discussed before). Overall, the population does seem to be fairly young with very few tracts having more than about 30% of its population over the age of 65. At the Metro area level, the mean population density for the 65+ demographic was 10.6%. Similar data was found when comparing this to the buffer areas around the stations. The station areas didn’t seem to have any distinguishing characteristics in terms of senior population densities and again, showed real variety in concentration levels. All in all, the population also seems to be predominantly young, the mean population density for the 65+ demographic within the half-mile buffer

of rail stations was just over 12%, slightly higher than the entire Metro area.

METRO STATION BUFFERS VS. AREA - Physical Disability Intensity Finally, the fourth buffer map created for this project (Figure 9) compares physical disability intensity levels at both the Metro and rail station buffer geographic ranges. Again, in order to display this data, I used the same physical disability index I described on page 3, which combines a series of variables describing physical limitations and displays intensity/concentration levels of the disabled population. As in the previous map, the darker oranges in this map illustrate census tracts with higher concentrations of disabled residents. At the Metro area level, it was found that concentrations of disabled individuals were widely dispersed and had an overall, or mean index, level of 234 (which I labeled as being in the Medium-High range.) When concentrating on the areas around rail stations, similar dispersal patterns were observed. Disability levels within the station buffers were also widely varied in intensity levels with pockets of higher disability intensities


Map, Metro Station Buffers vs. Area Senior Population Density (Sources: LA Metro, 2010 U.S. Census) PAGE 8

randomly dispersed throughout each of the station areas. Here, the overall mean index level for the buffer areas is 250, which is just slightly higher than that of the Metro area average (but is still within the labeled Medium-High range.)


250 (Medium High) METRO AREA MEAN INDEX:

234 (Medium High)

(Medium- Low) (Medium- High) (High)


Map By: Carla Salehian Source: SimplyMap, LA Metro


All in all, the four buffer maps created for this project displayed interesting information in terms of the existing demographic conditions at both the Metro area and the rail station levels. Some variables, such as “Medium Income” and “Transit to Work” had definite spatial characteristics in terms of how density levels were dispersed across the area. Most notably, downtown seemed to contrast the areas at its periphery. Other variables, “Senior Population” and “Physical Disability Intensity,” seemed to have been dispersed at random with no clear spatial distinctions. Instead, the most notable trend overall was that the differences between the rail station areas and the larger Metro area were predominantly insignificant. In many ways, this information came as a bit of a surprise to me as I was expecting rail station areas to be slightly more distinct, especially in terms of age and disability demographics. Specifically, I expected the rail station area populations to be much younger and have lower disabled population

levels (considering their low ridership levels) when in fact, the opposite is true. Contrary to my presumptions, these maps seem to demonstrate that the stations were established in locations that are representative of much of the greater Metro area.


Map, Metro Station Buffers vs. Area Physical Disability Intensity (Sources: LA Metro, 2010 U.S. Census) PAGE 9


What’s the average distance to a Metro rail station?!

Distance Assessment

Does distance to a rail station affect transit ridership?!

320 tracts!


347.8 feet!


3.9 miles!


1.1 miles!

Distance (meters)!

1.1 miles!



Chart By: Carla Salehian Source: 2010 U.S. Census, LA Metro


Pct. Public Transit Users!

Public Transport Usage & ! Proximity to Metro Rail Station!

After comparing the demographic characteristics of both the Metro area and rail station buffers, my next step was to perform spatial analysis type studies in order to answer my second general research question, “What is the average distance to a rail station?” In order to respond to this seemingly simple question, I would have to calculate approximate distances for each census tract by finding their centroids, or center points, and use what ArcMap calls “Straight-Line Distance” in order to determine a measurement between these two points. Of course, this method is a bit flawed in that it makes broad generalizations in terms of actual distances people have to travel to get to a rail station but nevertheless, seemed appropriate considering the limited data I had to work with in the first place. In order to conduct this analysis I began by projecting the Metro census tracts and then used the “Feature to Point” tool to calculate the census tract centroids and saved the output point shapefile. Next, I created a spatial join between these centroids and the Metro Rail stations. After doing this, ArcMap

automatically created a Distance field that describes the distance between each census tract centroid and the nearest Metro rail station. Using this information, I was able to generate overall statistics and converted the distances listed from meters to feet and miles. Of the 320 census tract centroids, the minimum distance to a rail station was a mere 347 feet. The maximum distance was 3.9 miles and the average was just over 1 mile. To further assess these distance statistics, I compared them to “Percent Public Transit Users” variable and created a table within the ArcGIS platform (and later Photoshopped the graphic) to find that distance to a rail station, does seem to have a significant impact on public transportation usage (See Figure 10).


Chart, Distance Assessment (Sources: LA Metro, 2010 U.S. Census)


METRO DISABILITY HOT SPOT ANALYSIS Areas in Most Need of Rail Stations! Where would future transit stations be located if they were to consider the needs of disabled users?!



Hot Spot Analysis: Areas in Most Need of Rail Stations Up until this point, the maps I’ve created for this project have illustrated existing demographic conditions in the Metro area, the next pair of maps compile this information and make projections for proposed changes in the existing rail infrastructure. Specifically, Hot Spot analysis was used to answer the question of where future rail stations should be located if they were to consider the needs of disabled users. To do this, I formulated a list of four variables from previous analysis that I believed to be critical for requiring new stations:

1. Distance to a nearby station: The greater the distance to a higher station, the higher the need. 2. Median Income: The lower the median income, the higher the need. 3. Percent of Population 65+: The higher senior population, the higher the need. 4. Physical Disability Index: The higher concentration of disabled individuals, the higher the need. After this, I made use of the ArcMap Model Builder once more to convert my distance and demographic census tract shapefiles to raster layers and classified the symbology for each of the new layers (See model below). Next, in order to use the Hot Spot Analysis tool, I reclassified my variables in order to assign my criteria (or a preference range

from 0-5) for interpreting the variable values (See model below. For instance, in terms of median income, those in the lowest range were given a preference value of 5 and those in the highest range were given a preference value of 0. In the end, the last step was to index my raster layers in an unweighted formula as follows: “RailDistRcls” + “65UpRcls” + “DisIndexRcls” + “MedIncRcls.” In the end, what resulted was the map you see in Figure 11. Here, the black dots represent existing rail stations within the metro boundary with the areas in most need of rail stations in the darker red tones. Interestingly enough, one of the areas in the peripheral regions with the greatest need was in the far west, where the purple line extension is expected to pass through. Hopefully this could attract many elderly or disabled users.

AGE 65+



Map, Hot Spot Analysis: Areas in Most Need of Rail Stations (Sources: LA Metro, 2010 U.S. Census)

Map By: Carla Salehian Source: LA Metro, 2010 US Census, SimplyMap


Reclassify Model PAGE 11

METRO RAIL STATION Infrastructural Conditions Assessment!

Metro Rail Station Infrastructural Conditions Assessment

Which stations might have infrastructure conditions in need of refurbishment?!


Map By: Carla Salehian Source: LA Metro, 2010 US Census


While my Hot Spot Analysis map suggested possible locations for new rail stations that could better suit the needs of disabled users, my last map was created as a means of determining which existing stations might have infrastructural conditions in most need of refurbishment. In other words, given the scenario that transit agencies had the goal of improving transit station infrastructure for better accessibility for users with disabilities, this map takes a logical/quantifiable approach to determining which stations they should investigate first. In order to achieve this, I examined two variables that I thought impacted infrastructure conditions: the year the station was built, and median income in the area and I then made the broad assumption that older stations in lower-income neighborhoods will be in most need of repair. Research was gathered on this information from Metro’s website (station addresses, year built, etc.) and was then compiled into a table. In the end, what resulted was the map seen in Figure 12. Once again, we see the Metro boundary and all of

the census tracts within it, but this time, the tracts with lower incomes are in the darker grey tones (being that they are of most interest to us.) Next the Metro area rail stations shapefile I created to include the researched attribute data were added and are represented in different tones of red according to the year the rail station was opened. (Note: The majority of the stations within a 10year interval. In order to better illustrate the differences in age, I maintained the “Natural Breaks, Jenks” classification.) The darker red tones represent the older stations (again, being that they are of most interest to us.) Visualizing both these variables we were able to easily identify the three stations (represented with a black dot in the center of the station marker) that might be considered to be “At-Risk” in terms of their infrastructural conditions. From southwest to northeast, these are the Blue Line’s Pico station, the Red Line’s 7th Street/Metro Center station, and the Red Line’s Pershing Square station.


Map, Metro Rail Station Infrastructural Conditions Assessment (Sources: LA Metro, 2010 U.S. Census) PAGE 12



Conclusions Though there is little doubt the Americans with Disabilities Act was a seminal piece of legislation that accomplished great things for the civil rights of persons with disabilities and acted as a major catalyst for creating more accessible public transportation, it is also very clear that much remains to be done in terms of creating truly accessible public transportation. Specific to bus and rail, it was identified that two of the major barriers to accessing these forms of transportation for users with disabilities are long distances to stations and poor infrastructural conditions. With these challenges in mind, this project sought to answer the general question of whether existing rail Metro systems are meeting the needs of Los Angeles’ disabled population. In addition, it sought to answer a range of specific research questions on the topic of whether demographic discrepancies exist between the rail station areas and the Los Angeles County Metro Service Planning Area, at large, what the average distance to a station is, where future stations should be located, and

which stations might have infrastructure conditions in most need of refurbishment. I answered each of these questions by creating and analyzing a series of maps. In the end, I found that the differences between the rail station areas and the Metro boundary were rather small, but larger differences were found when comparing the Metro service area to the greater Los Angeles County region. In addition, I performed a distance assessment to find that the average distance to a station from each census tract was just over 1 mile. Analysis of the existing conditions therefore suggests that rail stations within the Metro boundary seem to exist in locations largely representative of the greater Metro area and do not appear to be located in extremely distant locations. In terms of looking forward, Hot Spot Analysis was performed to identify areas in greatest need of transit stations and further analysis on existing rail station characteristics helped determine the transit stations with the greatest potential need for infrastructural improvements. That said, further steps could be taken to improve this project further. Particularly, in expanding the boundary to a larger region, larger discrepancies

in demographics might become more apparent. In addition, all the indices created for this project were unweighted. Creating weighted averages could contribute toward an even more accurate study. Overall, the issue of transit and accessibility is clearly complex, making it understandable as to why local municipalities and transit agencies have difficulty in approaching these issues. However, this project demonstrates the ways in which GIS can be utilized to help approach the intimidating problems regarding accessibility and transit through quantifiable methods and can break down action plans into more manageable phases, which is critical because improved transit infrastructure and accessible public spaces do not only directly benefit transit users with disabilities, they improve the health and livelihood of us all.


Screenshot, ‘Original’ Metro Area Rail Stations Shapefile Metadata


Author’s Note: These maps work in conjunction with my capstone thesis, Overcoming Infrastructural Barriers to Bus Transit Accessibility. Source materials and a detailed theoretical explanation of the sucject can be found in my thesis proposal.

References: KOPPA, R. J., DAVIES, B. and RODRIGUEZ, K. (1998): “Barriers to Use of Transportation Alternatives by People with Disabilities”. Texas Transportation Institute, Southwest Region University Transportation Center, Texas A&M University System, College Station. ROSENBLOOM, S. G. (2007): “Transportation Patterns and Problems of People with Disabilities”. In The Future of Disability in America (M. J. Field and A. M. Jette, eds.), National Academies Press, Washington, D.C., 2007, pp. 519–560.


Transit and Accessibility  

An exploration of the methods by which GIS can be used as a tool for transit agencies to improve accessibility for users with disabilities.

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