For this project I was able to easily acquire data sets all mainly from MassGIS (see Data Sources). The particular challenge with this analysis, however, was making the correct assumptions and decisions when it came to normalizing and what exactly my choice was arguing. With such an abundance of data, the difficulty was sifting through it all and choosing which layers to join and analyze together. Generally speaking, many of the classic urban economic features were found in the data. The monocentric city model offers an economic definition for a city and allows one to analyze its functions based upon it. In this case, all the transit analysis showed a higher demand for transport outside the city while the densest transit was found in within what the model defines as the Central Business District (CBD). Additionally, the major roadways and above average sales map showed how highway development somewhat dictates spatial patterning of expensive homes. Although my analysis was solely going to make claims about the Boston Seaport District, the following list will touch on the preliminary findings in this project and potential extensions of the research:

 

 

(1) Seaport District: Based on the analysis conducted, I would recommend further research be done on the transportation network, with particular consideration for the roadways. The issue of congestion in this area could be the bane of its economic success and deter new residences from moving there. As part of the data I collected, there are several Excel documents that outline average traffic totals for various traffic counting locations. The data set is a bit shotty and has several gaps, but the next step to better understand the real flow of people in and around Boston would be to join these traffic attributes to the physical locations on the map. Then one can better assess the roadway traffic versus the MBTA usage to figure out how to best mitigate congestion and delays.

 

(2) MBTA Transit: The MBTA has raised fares over the last few years and according to their statistics ridership has decreased across the board. However, their railroad transit lines are a necessity for a large portion of commuters who are coming from 50-60 miles away. A next step if I were to focus on the transportation effiency of the Boston area, would be to manually create datasets based on the MBTA ridership statistics and join those to the spatial attributes. Additionally, I would collect data on the costs to the MBTA and generate a spatial model of transportation that would carry both congestion and cost data. Perhaps there are transport solutions that cannot yet be seen but could be with a GIS.

 

(3) Boston Metro Real Estate: Another extension of this research will be to tie the population attributes to the census tracts and go deeper in understanding the necessities and variations in transport behaviors of different neighborhoods. From a real estate development standpoint, this would be crucial to knowing how to market certain projects to their target demographics. At present, Waltham seems to be a budding market where average home prices are still much lower compared to the neighboring suburbs. Based on the transportation and property value assessments, any location where a low average home price spatially correlates to a high level of transportation accessibility would be a prime location to consider a real estate investment. Additionally, another qualitative step could be to tie images of the properties to the data and find commonalities in the architecture of similarly priced homes and condos. This would indicate the level of price and one could use the images to show how expensive the Boston Metro area is compared to other areas of Massachusetts.