The goal of this analysis is to look at commercial parcels in the City of Boston and to evaluate them through queries to see if they match criteria set by the LEED Sustainable Sites category. Although there are other criteria for being awarded points in the Sustainable Sites category (such as considering distances to floodplains or wetlands), this project focused on access to public transportation and brownfield redevelopment. According to the LEED checklist for points on New Construction in Residential and Commercial parcels ( http://www.usgbc.org/ShowFile.aspx?DocumentID=3998 ), one point is awarded for a new building being built on a brownfield, and one point is awarded for a new building being built either within a half of a mile of a subway or train stop or a quarter of a mile of a bus stop.
Select by Attributes – Selected Parcel data by land use code attributes
Both the land use codes “C” and “CL” are Commercial, while “RC” is used for residential-commercial mixed-use.
Select by Location – Selected Parcel data by proximity to subway and bus stops.
All commercial and mixed-use parcels within 0.5 miles of a subway stop and all commercial and mixed-use parcels within 0.25 miles of a bus stop were selected.
Select by Location – Selected parcel data based on intersection with brownfield sites.
Using ArcCatalog to create a personal geodatabase from the previous parcel layer, an address locator was created. Using data on brownfields found on the Mass DEP website ( http://www.mass.gov/dep/cleanup/sites/sdown.htm ), addresses of brownfields were then geocoded to the parcel layer. Next, all commercial and mixed-use parcels that intersected these brownfield sites were selected.
Field Calculator – Converted text field Year_Built to numeric field Cons_Year
In order to perform a statistical analysis of the original year of construction of the parcel, the field calculator was used to convert the text field into a numeric field that could be manipulated.
This summary table bins all 412 parcels by zip code and shows the average building value and average land value for each.
Some of the more obvious problems concern the attributes of the data. One such problem is that many of the parcels were recorded to have been built on in 1997 or 0 (two years that were probably used as fillers), and thus any information based on this summary is not reliable. If one were interested in looking at parcels that might need to be redeveloped soon (with LEED certifications in mind), better data would be needed. Another problem involves data quality assessment. As I learned in the last assignment, locations for bus stops and T stops are not always accurate with respect to other data layers. Thus, there may be parcels that would actually receive the public transportation access points that are not shown in this project, or possibly some parcels may have been selected that are in reality too far away.