ChristopherTimmel

UEP 232 | Introduction to GIS

Assignment 1 – Interests and Spatial Questions

September 13, 2013

TOPIC 1 – MBTA BUS RELIABILITY

Over the summer, and hopefully continuing this fall (fingers crossed!), I had a position with the MBTA Advisory Board where I was tasked with tracking dropped trips on both bus and rail. That is, how many trips did not go out according to the MBTA’s posted schedule? There is a significantly higher percentage of dropped bus trips vs. rail trips, but at this point data only shows bus by garage or starting point. If possible, I would like to breakdown this data by bus route to track what neighborhoods within the MBTA’s service area are being most affected by these dropped trips.

To complement this data, I believe that demographic data around ethnicity and income would be telling to paint a picture of which groups of people are being most affected by inefficient transit. Knowing that bus trips are more likely to be dropped, I assume that those who traditionally have poorer access to transit are the same people who are most affected by unreliable bus service. This is a potential thesis project, so some of this information may have to be manually synthesized and added to existing data layers.

Questions

  1. What bus lines within the MBTA system report the highest number of dropped trips, and which neighborhoods in the Boston area do each traverse?
  2. This data would be foundational for this particular project, as it would be tracking specific bus lines as opposed to the total number of trips that we have at this time.
  1. What is the ethnic and income makeup of the Boston metro area?
  2. By knowing this demographic information I’d be able to better frame the dropped bus trips data, showing what populations are being more poorly served than others.
  1. Where there are a high number of dropped bus routes, which areas have other options available for public transit?
  2. Knowing transit accessibility statistics could be useful to see to what level certain populations are affected by a certain dropped trip. Someone waiting for a bus at a stop that has multiple options is different than someone is dependent on just one bus and who is a long walk for other transit options.
  1. What is the daily ridership of individual bus lines, and therefore, about how many people are affected by dropped trips?
  2. By knowing the number people who use unreliable bus lines, I could estimate the number of people who are inconvenienced on average over the course of a year.
  1. By zip code, where is the highest frequency of MBTA complaints coming from, and do these correlate with dropped trip statistics?
  2. If the MBTA’s complaint database tracks addresses or zip codes, I think this could interesting to geocode for and see how it relates to most unreliable bus routes and the neighborhoods affected by this unreliable transit service.

References

Lao, Yong and Lin Liu. 2009. “Performance evaluation of bus lines with data envelopment analysis and geographic information systems.” Computers, Environment and Urban Systems, 33: 247-255.

This article provides an argument for not only measuring bus specific transit performance, but the value in, and methodology for, combining different types of statistical information to base conclusions from.According the authors, “the best way to obtain a comprehensive picture of bus line performance is to compare operational efficiency with spatial effectiveness (253).” The authors tracked data such as annual ridership, round-trip distance, number of stops, and population with disabilities, which is either on par or related to what I plan to look at.

Lei, T. L. and R. L. Church. 2010. “Mapping transit‐based access: integrating GIS, routes and schedules.” International Journal of Geographic Information Science, 24, no. 2: 283-304.

This study provides another comprehensive look at tracking and mapping public transit data, as well as its importance for local/regional governments when determining priority areas to improve transportation service. Though focusing on transit access, the rationale and methodology of this study will be helpful when conducting this project. Another interesting part of this study is that is concentrates not only on distance to a transit stop, but on the time it takes for someone to get from point A to point B, and how the estimated time matches with the actual time of travel.

TOPIC 3 –URBAN CONNECTICUT WEALTH OVER TIME

Being from Connecticut, I have an interest in its history and geography. At one time its older industrial cities were quite prosperous, but with industry changing over time, so too has its economies. Connecticut is considered one of the wealthiest states in the country, but its urban areas are often listed as some of the depressed (and dangerous, according to Business Insider). With this, I thought it would be interesting to use census information to track wealth over time, not only in Connecticut’s urban areas, but also in relation to its neighboring suburbs.

Questions

  1. What Connecticut cities have witnessed the greatest population change over past century?
  2. Of these cities, how has average income or property value changed over time?
  3. Of these cities, how has industry changed over time?
  • Addressing these series of questions I think would be a good way to illustrate the history of Connecticut’s development over time as industry changed and income inequality became more extensive. It would also be interesting to connect this to regional and national economic and demographic trends over time, described not necessarily in maps but in the narrative.

References

Orfield, Myron and Thomas Lucre. “Connecticut Metropatterns: A Regional Agenda for Community and Prosperity in Connecticut.” AmereGIS, Minneapolis, MN, 2003..

This document summarizes many different trends across the state, from income disparities, to shrinking and growing small cities and older suburbs, property tax bases, % of jobs, development of sprawl, etc. Though focusing on changes between around 1980-2000, the types of information used in this analysis will be helpful when considering what types of data will be most useful when conducting this project.

Orford, Scott. 2003. “Identifying and comparing changes in the spatial concentrations of urban poverty and affluence: a case study of inner London.” Computers, Environment and Urban Systems, 28: 701-717.

This article is interesting in that is compares both concentrated poverty and wealth in central London between 1896 and 1991. This is particularly interesting because there is an historical GIS analysis that seems to be rare among peer reviewed articles. This may be because authors do indicate this type of analysis is difficult because data from over 100 years ago could be incompatible with modern data – whether it’s the type of data measured and how it’s measured/interpreted, or whether any useful data exists at all. The dataset here is limited to income and one city, but it shows it’s likely that this can be expanded to include a larger area and other datasets if available.

Reardon, Sean F. and Kendra Bischoff. 2011. “Income Inequality and Income Segregation.” American Journal of Sociology, 116, no. 4: 1092-1153.

This study is of interest to me because the subject areas fall in line with the potential study at hand. That is, the patterns of poverty and affluence, race, and income segregation on a geographic scale. Though the researches did not conduct GIS analysis, the information they collected seemspossible to map. This study was also a general analysis of trends nationwide using U.S. Census data, but data on the national census level should be easy to retrieve at the state level. The only thing that the researches expressed concern about was that, depending on the years being tracked, the type of method of data collection may not line up perfectly. Overall, this seems like a study that could valuable to me if I focus on the project above.

TOPIC 2 – URBAN TREE CANOPY AND PROPERTY VALUE

Having an interest in the beneficial effects of urban green space, it would be useful to measure either the number of street trees or percent canopy cover of Boston (or some other major urban area) and correlateit with property value. I was also attracted to this topic area after hearing a professor talk about the fact that after the city added street trees to certain parts of the South End, values went up and the lower income people moved out. I have found a number of studies on access to parks/green space, which I think would be helpful to model, but there seems to be room to study tree canopy or street tree quantity in relation to a number of demographic measures.

Questions

  1. Beyond general property value trends within the city of Boston (e.g. Back Bay, Beacon Hill vs. Roxbury, Dorchester), getting down to street level, is there a difference in property value depending on the proximity to street trees?
  2. If so, does the size/canopy, health of the tree, or sheer number of street trees affect property value?
  • I think these questions would be very interesting to research, as we might some interesting property value trends related not only to canopy cover, but the number of street trees of any size. One concern I do have is that other factors, such as proximity to parks or transit stops, may affect property value. Perhaps this study should focus on specific neighborhoods, rather than the entire city.

References

Donovan, Geoffrey H. and David T. Butry. 2009. “Trees in the city: Valuing street trees in Portland, Oregon.” Landscape and Urban Planning, 94: 77-83.

Not surprisingly, Portland has already done this type of study. However, while they do map house sales and find that proximity to street trees does affect value, they do not map trees in relation to property value. What is interesting here is that the study does consider great detail when analyzing value, from the tree species, whether it flowers, whether it’s diseased, whether it’s coniferous or deciduous, etc. There is plenty of data here that may be difficult to place on a map, at least a map that will be legible.

Sander, Heather, Stephen Polasky and Robert G. Haight. 2010. “The value of urban tree cover: A hedonic property price model in Ramsey and Dakota Counties, Minnesota, USA.” Ecological Economies, 69, no. 8: 1646-1656.

A similar study was also conducted in Minnesota, and while it doesn’t seem like the researchers are specifying the species and/or quality of the tree itself (rather, just percent canopy cover), they do realize that many other variables affect property value (e.g. schools, proximity to work, central business districts, busy intersections, scenic landscapes, etc.). With that, the researchers created two different models – one that factored in all potential variables affecting property value, and then one that factored in tree canopy. With that, tree cover within 250 meters increased home sale prices. I’m not sure whether or not this will be too complicated as an intro GIS project, but this study does provide a robust model with some options as to where I can go with it.