Let’s look at the 311 calls on tract level. How is the distribution and are there any areas showing specific spikes when it comes to COVID related calls?

As the first try, let’s plot the count measure of 311 data to simply see how the calls are distributed across census tracts.

In the first measure, I am only looking at the count of calls in each census tract. The following figure will show us the distribution of calls across census tracts.

## Reading layer `count_311_CT' from data source 
##   `/Users/Shared/Files From d.localized/School/summer 2021/count_311_CT.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 204 features and 15 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -71.19125 ymin: 42.22793 xmax: -70.64807 ymax: 42.45012
## Geodetic CRS:  NAD83


The distribution of calls across tracts shows a descending exponential function pattern. There is not a dominancy in numbers for just a few tracts. But there are considerable differences in the range of the calls across the geo level. Let’s map this same figure,


The whole downtown area, South Boston, South End and some areas of Dorchester are showing to be the CTs with higher number of calls. Would the same pattern be seen if we consider population?

## Reading layer `count_311_CT' from data source 
##   `/Users/Shared/Files From d.localized/School/summer 2021/count_311_CT.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 204 features and 15 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -71.19125 ymin: 42.22793 xmax: -70.64807 ymax: 42.45012
## Geodetic CRS:  NAD83


Normalizing by population changes the distribution drastically. We now have a few CTs with large number of calls and the rest of CTs with next to zero. Let’s map this.

The CTs with populations less than 200 cause specific CTs to turn red. Let’s drop those.


The Downtown, Seaport, South Boston and Franklin park area are noticable after applying the normalization. Let’s look at the COVID measure as the next step.

## Reading layer `covid_words_311_ct' from data source 
##   `/Users/Shared/Files From d.localized/School/summer 2021/covid_words_311_ct.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 204 features and 15 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -71.19125 ymin: 42.22793 xmax: -70.64807 ymax: 42.45012
## Geodetic CRS:  NAD83


For COVID measure, there is one CT with high number of calls and the rest of the CTs are following in a decreasing rate. We can start mapping the COVID measure now.


The downtown and seaport area are the places with the highest calls. Although these two are the top ones, there is a noticable difference between them. Let’s normalize the measure now.

## Reading layer `covid_words_311_ct' from data source 
##   `/Users/Shared/Files From d.localized/School/summer 2021/covid_words_311_ct.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 204 features and 15 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -71.19125 ymin: 42.22793 xmax: -70.64807 ymax: 42.45012
## Geodetic CRS:  NAD83

The same pattern is apparent with a few tracts having high number of calls and the rest next to zero. The next step is to map this measure.

Again, the impact of low populated areas is notocable. Let’s drop them and map again.

We can see the red spark all over downtown, seaport and south Boston along with Franklin park. Compared to BG analysis, COVID measure is more noticable in the CT level.
Let’s look at the Violation measure constructed using the COVID violation reports in 311 data.

## Reading layer `violation_words_311_ct' from data source 
##   `/Users/Shared/Files From d.localized/School/summer 2021/violation_words_311_ct.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 204 features and 15 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -71.19125 ymin: 42.22793 xmax: -70.64807 ymax: 42.45012
## Geodetic CRS:  NAD83


The same wide range and distribution pattern is apparent in this measure as well. Let’s map it.

Several tracts of south Boston show as the top CTs with highest number of calls. Downtown area is alsoglowing up to some extend. Let’s normalize by population.

## Reading layer `violation_words_311_ct' from data source 
##   `/Users/Shared/Files From d.localized/School/summer 2021/violation_words_311_ct.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 204 features and 15 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -71.19125 ymin: 42.22793 xmax: -70.64807 ymax: 42.45012
## Geodetic CRS:  NAD83


We see a few CTs with high numbers of call once more. Let’s map this.


And filtering for the areas with population higher than 200:


On CT level, Frankil park shows the higest rate of violation calls. After that, the South Boston area glows up the with considerable difference in comparison with Seaport, Sounth End and Downtown area. We can see somehow the same distribution on BG level as well.