Ana Torres November 3, 2011

Assignment 5: Basic queries

MBTA Red Line Repairs

Starting next Saturday (November 5, 2011) and until March 4, 2012, the MBTA will close the Red Line north of Harvard Square on weekends to complete $80 million in repairs designed to keep trains from derailing due to eroded tracks and power lines caused by leaks in the tunnel.

The service will be replaced with shuttle buses. According to an article from the Boston Globe by Eric Moskowitz on October 22, 2011, about 21,200 riders on Saturday and 14,200 on Sunday board the Red Line at Porter, Davis and Alewife stations, meaning over 35,000 commuters will be affected weekly in the five-month shutdown period.

The MBTA has come up with its next commuter nightmare. On weekends starting November 5, Red Line trains north of Harvard Square due to tunnel repairs. According to an article from the Boston Globe, the $80 million project will serve to keep trains from derailing

These events are a perfect opportunity to reanalyze the profile of the users of the MBTA. Having a clearer idea of the sociodemographic profile and the patterns of use of transportation of the people that will be affected by this project will help to have a better assessment of the true cost of the project, that it, not only the monetary cost of the infrastructure but counting the social cost of the project as well. For this, a map that relates different characteristics of the population and their transportation information will result extremely useful.

For the map, I focused on Cambridge and Somerville since these are the neighborhoods where the three T stations that will be closed (Porter Square, David Square and Alewife) are located. All my data layers come from the MassGIS file at Tufts GIS Center.

As a first step I added the boundary_poly layer and selected by attribute (‘Town’) Somerville and Cambridge for the map. I then added the mbta_arc and mbta_node layers. From the first one I selected the stations of the Red line only as Figure 1 shows. From the second I selected four stations: Harvard Square and the three that will be repaired (Figure 2). I included Harvard because it is the last station that will work during weekends so it is important to place it in the analysis. Then, since the mbta_arc layer is formed by pieces I select the only that corresponds to this four stations.

As part of the mitigation strategy, the MBTA will run buses between stations but it is important to see if there are already bus routes that connect the stations. For this I added mbtabusstops_pt and mbtabusroutes_arc, and then selected by location the routes pass close to the red line. I selected the features that are within a distance of 500 feet as Figure 3 shows.

Figure 1. Selecting the red line by attribute

Figure 2. Selecting the affected stations by attribute

Figure 3. Selecting near bus routes by location

By selecting the bus routes that passed near the stations, some of the routes that initiated in the stations but did not connect to the other stations where selected. So, by selecting route by route I detected the ones that connected at least two of the stations resulting in the routes marked in blue in Figure 4. This simple analysis shows that Harvard and Porter Stations are well connected (Chart 1); they have six routes that pass within walking distance from them. Davis is also connected having three bus routes connecting with Harvard and Porter. However, none of the routes connects with Alewife Station.

Chart 1. Bus routes connecting the affected stations Bus Route Harvard Porter Davis Alewife 71 73 77 83 87 96

Figure 4. Bus routes and stations within walking distance of the segment of the red line that will be repaired.

I then added the layer census2000blockgroups_poly and the tables from the Census 2000 by blockgroups housing_amen_ten, income_hh, legattrib, trns_com_means and trns_com_time. I joined all the information from the tables by joining them to the census2000blockgroups_poly layer. The purpose of joining the information was to set the profile of the people living near the stations, which is the population most affected by the reparations project.

Given the fact that Alewife station is in an isolated situation compared with the other stations, I wanted to see the population density of the area. For this I mapped the density (total population over dry square mile). As expected the other three stations Harvard, Porter and Davis are more densely populated than Alewife (Figure 5). Then I wanted to have an idea of the median income of the people living in the area. I map the variable inc_med_hs and used the Statistics tool for the inc_med_hs and showed that the average median income of the block groups of Somerville and Cambridge is $49,954 as Figure 6 shows. Figure 5. Population Density

Figure 6. Statistics of Income of the Households in the area.

Then, using the information from the census I wanted to see from the total population, was the percentage of people 65 years or older. I added a new field called perc65 and then used the Field calculator dividing the population over 65 or older over the total population (Figure 7), however, there is a block group near Harvard Station that reports a total population of zero, therefore the field could not be calculated since this information is missing from the source.

Figure 7. Adding and calculating a field: percentage of the population 65 or older.

Then I decided to analyze the commuting time and means of the people in the area. My first idea was to create a variable of the people that commutes 30 minutes or more, so then I created again a new field com_mins_30p (Figure 8) which is the sum of the people who had a commuting time from 30 to 34, 35 to 39, 40 to 44, 45 to 59, 60 to 89 and more than 90 minutes (Figure 9).

Figure 8. Adding the field com_mins_30p

Figure 9. Creating the value of the field com_mins_30p

However I realized that the information from the census refers to the time commuting to work and since the project is on weekends the information is not useful. I do not have the information needed to make an analysis of the profile of people because I don’t know how the use public transportation or their own vehicles on weekends. I then decided to check from the block groups with an income less than the average (Figure 10) how many households did not own a car and therefore needed public transportation on weekends if they go to a place beyond a walking distance (Figure 11).

Figure 10. Selecting households with income less than the mean of the area.

Figure 11. Statistics from the households that do not own a car and have less than the average income of the area.

Finally I concluded that that doesn’t give much information either. Maybe the household don’t have a car because it is a non-family household of students living near campus or a household were work is near and so is not necessary. Therefore the effect that the closing of the red line stations on weekends will have is going to be different from the people for example that works on weekends and has to commute. This information is relevant for a good analysis but I don’t have it.

So, as my last attempt I selected the block groups that intersect the red line and then manually selected the ones that do not intersect but are near it (Figure 12). Then created a field called affected which is a dummy variable that takes the value of 1 if the block group is in this selection and 0 if it’s not (Figure 13). I then summarized the average income of the households by these categories (Figure 14).

Figure 12. Selecting block groups in the red line

Figure 13. Creating the dummy variable affected

Figure 14. Summarizing income by affected categories

The output was the table in Figure 15. It shows that the average income is higher in the areas near the red line than the one that are not. I guess this is consistent with the process of gentrification, a discussion that I have heard related to the extension if the green line. However this does not give any useful information for the specific case of the project. I wonder were the Boston Globe writer got the data he mentions and it would be interesting to see if the MBTA did an extensive analysis of the people affected during this four months.

Figure 15. Output of summarizing tool