Proximity of Police Stations to Free WiFi Locations in Boston

The map below shows lines connecting police stations to free WiFi locations in Boston, Massachusetts. The table below shows what police stations are within proximity to the WiFi locations.

Overall, the District B-2 has the most connections to the WiFi spots. If there was an emergency at these locations, officers from B-2, C-11, and A-1 are more likely to respond faster than A-15, D-4, or D-14. Its seems that there are more police stations clustered in the city that have more connections to the free WiFi locations, rather than the police station in Hyde Park, or Newton.

The data was collected from "Analyze Boston," and the map was created on ArcGIS Online.

Drive-Time Analysis in Boston for Public Schools

One analysis shows driving time within three minutes (without traffic) to public schools (shown in light blue). The second analysis shows driving time within three minutes (with traffic) to public schools (shown in light green). The second analysis has a traffic pattern that resembles typical Monday traffic conditions at 7 A.M.; usually when people are on their way to work.

Based on the map, there is an overlap between the two analyses in the center of the city, which is typically the busiest and most congested. The area for driving time with traffic to public schools is shown more than the light blue, indicating that people may use different routes that are out of the way to reach public schools during that time.

The data was collected from the "Analyze Boston" site, and ArcGIS Online was used to create the map.

Lab 1: ArcGIS Online Map Tour

Purpose- 
The purpose of this lab was to practice developing a data set and an ArcGIS Online Map. 

What's needed for the lab-

• Seven unique stops/sites for a tourism map for a hypothetical trip
• Access to ArcGIS Online/an ESRI account
• The locations need latitude/longitude coordinates; an informative website; photo

Design Process- 
Using ArcGIS Online, the map was created by using the given latitudes and longitudes of the different sites. The pop-up windows were reconfigured to show the name of the site, the city, a link to a website that provides information on the site, and lastly a picture of what it looks like. Because the locations differ, I changed the symbology so that each site had their own color; this made the map easier to read.

Lab 2: Environmental Justice in Lexington, Massachusetts

The EPA defines environmental justice as: "the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income, with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies."

Description of the Lab:

This study was to determine environmental justice and non-environmental justice areas and populations in Lexington based on data from Mass GIS. The map provided shows environmental justice and non-environmental justice areas, as well as toxic waste sites that are within 2000 meters of these areas.

Figure 1:

How it was done:
First, data of environmental justice populations was obtained from Mass GIS. Next, in ArcMap, the layers provided from the data obtained were added. The area of the town was first calculated. Next, the Environmental Justice layer was used, and “clipped” to the town; this clip allowed me to analyze how many areas of environmental justice populations there were in the town, as well as the total area. The geometry of the layer was converted to square kilometers. Next, I hand-selected the 2000m area around the town that featured the toxic sites by simply drawing a square around toxic sites within the town as well as surrounding the town. Next, a “buffer” was created for the environmental justice areas within 2000 meters of the town. This buffer allowed me to analyze and compute the total number of environmental justice areas within 2000m of toxic sites in the town of Lexington, as well as non-environmental justice areas.

Table 1:

Location (Town, EJ, Non-EJ, etc.)	Area (sq km)
Town	43.092
EJ Area	25.72
Non-EJ Area	17.37
EJ within 2000m buffer	20.00
Non-EJ within 2000m buffer	11.08

Table 2:

Location (Town, EJ, Non-EJ, etc.)	Percentages
EJ Area	59.70%
Non-EJ Area	40.30%
EJ within 2000m buffer	77.8%
Non-EJ within 2000m buffer	63.79%

*For each of these tables, EJ stands for Environmental Justice.

Results, Conclusions, and Caveats:

Based on Table 1, there is a larger area of environmental justice than there is for non-environmental justice. The total area in Lexington is 43.092 sq. km. Out of the total area, 25.72 sq. km. are environmental justice, an overall 59.70%. The remaining 17.37 sq. km. of the town is non-environmental justice, and roughly 40.30%. Within 2000m of toxic sites, 77.8% of environmental justice areas are present, and 63.79% of non-environmental justice areas.

The results conclude that there overall are more environmental justice populations than there are for non-environmental justice. The data shows that a large percentage of the environmental justice areas occur within 2000m of toxic sites, and there are more environmental justice sites than there is non-environmental justice. There can be further research to study rivers and waterways to determine if there are any volatile chemicals present; and these studies in the water can help identify if there are any environmental justice populations or non-environmental justice populations affected by the contaminated water. When mapping this data, it would also be useful to determine how these cities compare to those directly surrounding them, or comparing them to other towns with similar demographic attributes.

Lab 3: Assistant Accessibility at Bridgewater State University Bus Stops

Description of application:

My project partner, Jocelyn, and I created an editable mapping application for Collector for ArcGIS for Bridgewater State University. This application allows students to create maps that can be edited by other classmates/visitors; it allowed us to create a map to be used for students and faculty to determine where bus stops are located on campus. These bus stops are either located in residential, commuter, or faculty/staff lots. In each lot there are “blue phones” which are there to help people who are in trouble and need to contact an emergency number. Overall, this application helps people know how many phones are within view of specific bus stops, just in case if they need them.

Table 1:

Field Name	Type of Field (Text, Float, etc)	Values
Team_member	Text	Michelle Boretti or Jocelyn Rua
Location	Text	Street, Commuter Lot, Residential Lot, Faculty/Staff Lot
Route	Text	Route 1, Route 2, Route 12, Route 1 and 2, Route 2 and 12, Route 1 and 12, All Routes
blue_lights	Float	Range (0-5)

How it was performed:

            Using ArcGISOnline, as well as the app Collector, we were able to create our own map based on information about BSU’s campus that we thought was helpful.  To do this, we created a new Geodatabase, and created different fields that would be used to describe what is being used for the map (for example, our names, the lot type, etc). We created types for these fields (text, float), and a description for each. We shared our geodatabase with the rest of the class, and on ArcGISOnline we created the map with pop-ups that show different points on our map that represent bus stops. On our cellular devices, we downloaded the ArcGIS Collector app which allowed us to add our own points, using the fields that we created.

Conclusions:

            At first, I had trouble collecting the points due to an accuracy issue on the app. It constantly said I had poor accuracy, even though my phone’s locations services were working properly and that I was standing exactly where I needed to be. (I eventually was able to collect the points that were needed). For our class it seems as though a majority of the students had similar maps. It would be interesting if we expanded our research, and were able to collect different points around the town of Bridgewater that could appeal to the college students. For example, coffee shops, restaurants, and places that are well-lit. This application has a lot of details, and it could also be useful if students who wanted to add to a map that is not their own could also edit the maps symbols, legend, etc. to make improvements/add their own insight.

My Web Application Project:

To view my collector web application project, either click on the image below or the URL provided:

URL: http://arcg.is/2kXx0w7

Lab 4: Loss of Prime Farmland in Duxbury, Massachusetts

Description of the application:

The goal of this assignment was to calculate the loss of farmland soils in Duxbury, Massachusetts due to an increase in impervious surfaces by using raster data. With an increase in urban development, there are more impervious surfaces taking over the town of Duxbury, leading to a decrease in farmland soils that are considered “prime farmland,” “farmland of statewide importance,” and “farmland of unique importance.” Calculations were performed to determine how much of these areas are present in Duxbury.

Table 1:

	Count (sq meters)	Square Kilometers
Total study area size	62,326,600	62.33
Prime Farmland	4,702,600	4.703
Farmland of statewide importance	9,076,600	9.08
Farmland of unique importance	2,470,300	2.47

Table 2:

	Count (sq meters)	Square Kilometers
Prime farmland lost	484,600	0.48
Farmland of statewide importance lost	803,300	0.80
Farmland of unique importance lost	62,600	0.06

How it was performed:

This process was performed using ArcMap and raster data provided by the state of Massachusetts. The study area for this process was Duxbury (the town I chose), and shapefiles were created for the study area. The raster data provided were for Massachusetts soils, and a “Map Unit” table was provided that describes the different types of soils, and a field called “MUKEY.” A join was performed between the study area soil raster dataset and the Map Unit table with the shared “MUKEY” field to determine the location of the farmland soils. The statistics tool in the attribute table on ArcMap was used to calculate the “count” of the MUKEY, and the count field was converted to square kilometers from square meters (COUNT * 100 to get square meters). Next, the impervious layer raster was used and extracted to the study area. A combination was performed to determine the amount of farmland lost for the three categories, while another join was performed. This combination and join allowed me to get the COUNT for the farmland lost (Table 2).

Figure 1: A map of study area

Results and Conclusions:

Based on Table 1, Duxbury is 62. 3266 square kilometers. Of the entire area size, 4.7026 square kilometers is considered prime farmland, 9.0766 square kilometers is considered farmland of statewide importance, and 2.4703 square kilometers is considered farmland of unique importance. Based on Table 2, 0.4846 square kilometers of 4.7026 (approximately 10%) of prime farmland was lost, 0.8033 square kilometers of 9.0766 (approximately 8.9%) of statewide important farmland was lost, and 0.0626 square kilometers of 2.4703 (approximately 2.5%) of uniquely important farmland was lost.

We were visited by Maggie Payne who works for the USDA NCRS. According to Payne's presentation, the maps were made at different times, and the data was collected based on ground conditions. This is a limitation for the study because the ground conditions can change every day, and impervious surfaces are constantly being added due to rapid urban development. It would be interesting to determine why these farmland areas are being lost, for example, are condominiums being built, or are new parking areas being paved, etc. Determining how many parking areas and shopping malls are being developed on top of prime farmland can help conservationists to stop the excessive loss of farmland. Although 10% doesn’t seem like a high percentage of farmland lost, it would be better if it could remain less than 2%, in my opinion.