Below are several tables of calculated statistics that further guided the analysis within each ring, with special attention to the nearest features to employers. Many tools in ArcGIS generate spatial data statistics that can inform the analysis done with other tools. The first tool, zonal statistics as table, allowed me to assess the land use coverage within the entirety of the multi-ring analysis zone. Since the ring buffers do not share the same area, quantifying the total land use area of each zone lends itself to normalizing the data. By weighting the area of the multi-ring buffer and the land use coverage, this allows for a more accurate analysis. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Within the Boston Metro Area, the greatest land use activity tends to go to low or medium density residential being the most common. Proportionally speaking, residential properties take up the majority of all zoned areas. These zone types do not show the entire dataset, but are most relevant to trip generation. Another noteworthy statistic is that only 2.64% of the entire analysis circle has been zoned for land use. This would be a slightly underestimated percentage, however, since several portions of the rings cover waterways and the ocean.

 

Additional zonal statistics below quantify the transit resources and employment centers by ring. The results show that ring 1, or the CBD, has the highest employment and transit access concentration which reflects the monocentric city assumption. Moreover, the last column, "Percentage of Network" shows the percent of the feature's total in Massachusetts. The high percentages show that the Boston Metro Area requires a significant amount of state resource allocation.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Average Nearest Distance: Major Employers to Transit Features

 

Commuter Rail --- 0.119 miles

Highway Exit --- 0.024 miles

 

 

Another useful tool allows one to input points or polygons of one feature and generates a nearest distance to some other defined feature, even itself. A nearest neighbor index can be calculated, representing whether a data layer exhibits clustering, dispersion, or random placement. For example, the metrics above compare the proximity of major employers to highway exits and commuter rail stops. The average nearest distance acts as a benchmark when comparing smaller sub-samples, which in this case would be the analysis rings. By comparing statistics ascribed to these zones, it again acts as a quasi-benchmark. A key takeaway from the above numbers shows that, on average, the biggest employers in Massachusetts are closer to highway exits than they are to commuter rail stations. These numbers seem to indicate that a higher proportion of commuters to top employers use their car over transit. Nearest neighbors and distances are only limited in that the tool utilizes a linear distance, while an actual commuter would have to follow the street and rail grids.