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  • Assignment 6 - Juan

Juan Sanchez

Assignment 6

Basic Queries


Analysis goal.

The goal of this analysis is to determine which buildings larger than 50,000 square feet and older than 2007 are in need of retrofitting in the City of Boston . The different queries perform in this project will establish which buildings meet the criteria for retrofitting. Accessibility to retrofitting projects is also important as the residents targeted for this study is the immigrant Chinese community of Boston , population that relies substantially on public transportation.


The following data layers were used to for the analysis.

  • Parcels - City of Boston
  • Census Tracts and Block Group data.
  • Buildings – City of Boston


Select by Attribute


The following steps were performed:

  • Select - select by attributes
  • Select “year built” and “living area” to narrow down the buildings in Boston that meets that criterion.


In this query I determined which buildings larger than 50,000 square feet were built before 2007. This is extremely important for my project because in 2007 the City of Boston passed the Leed Certification Ordinance. This ordinance dictates that any building larger than 50,000 square feet will have to be Leed certified. On other words, I assume that all buildings built before 2007 will be in need of retrofitting work. Based on this query is quite clear where more retrofitting work should be done. For purposes of this assignment, I am only taking into consideration large buildings. Most of these buildings are large residential developments, retail, industrial, and office space. Further analysis for small buildings (residential) can be made in the final project.


As seen from map 1, most of the buildings that met my criteria are located in the       downtown area and the northern section of the city.













Map 1





Selection by Location


I used the query select by location to find out which of the building following my criteria (older than 2007 and larger than 50,000 sq/ft) will be more easily accessible by public transportation, more specifically, within a quarter of a mile of a subway station.

In map 2, it is noticeable that most of the large buildings in need of retrofitting in the city of Boston are within reach by subway. These are great news for Chinese construction workers that rely on public transportation to access job sites.

The steps followed to perform this query were;

  • Select - select by location
  • Selected the new layer created for buildings that met my previous criteria.
  • Created a buffer zone of ¼ of a mile around (have their centroid) subway stations in the city.









Map 2













Statistics for selected features


For this section of my project, I would like to see the statistics for the year built and the building living area in the city of Boston .  I performed these steps to see some statistics for the selected features:

In the layer that I created in step one, I selected the “living area” and originally, I was planning to select the “year built”. However, for the “year built” attribute does not make sense to get statistics from as it would only summarize, sum, or average numbers, not the actual years.

As shown in the following table, there are plenty of buildings that are in need of retrofitting. A large section of these buildings are in the downtown area and other nearby neighborhoods like the South End, and Chinatown . The chart below shows a count of only 662 buildings that meet the criterion, with an impressive total of 121,118,584 square feet in need of retrofitting.





Summary of an attribute


In this section of the analysis, I wanted to determine to which land use category these buildings belong to. This is the classification of the land uses for the selected buildings.

A: Residential (7 units or more)

C: Commercial

CM: Condominium Main (building broken into condo units)

E: Exempt

EA: Exempt (chapter 121A)

I:  Industrial

RC:  Mixed Residential / Commercial




The following steps were performed:

  • Right click on “LU” column of the attribute table
  • Click on “summary tool” to obtain the summary of the land uses of the buildings selected.


As seen in the table, commercial, exempt (government ?) and industrial buildings are the dominant structures in Boston , while residential (smaller buildings) and condominiums are smaller in space occupied. Nevertheless, still a significant portion of the large size buildings that need to be improve for energy efficiency.



Field Calculator


For this query I could no think of an actual relevant task I could perform for my project.  However, I decided to “fix” the gross tax in the 2009 parcel layer, just to practice the query. However, knowing the correct tax value could be relevant to determine the approximate value of the property. In a logical analysis, a larger building that pays more taxes would have more area in need of retrofitting.








The queries performed provide a good sense of the amount of square footage that is in need of improvements in the city of Boston . This analysis provides a first approach to the question of job accessibility and the mobility issues for Chinese workers in Boston . However, although most the data used in these queries came from an updated data file from the “assessors’ parcel layer of 2009” of the City of Boston , one can not assume that the data is complete. For instance, some of the “exempt” buildings do not provide enough information to identify them as a specific type of land use. Therefore, it is not accurate to include them in any specific category. This element becomes important if Chinese workers decide to target one specific building type as their job site niche.

Another portion of the data that was incorrect was the “gross tax” records. As I stated previously, if these numbers are inaccurate it could affect the analysis if this was part of the criterion selected.