Group: Val Healy, Tuyen Bui, Hayley Song

    For our data hunt, we chose to examine the 2013 Boston Employee Earnings dataset (https://data.cityofboston.gov/Finance/Employee-Earnings-Report-2013/54s2-yxpg). This dataset includes city workers’ names, title, department, earnings (broken down by type), and zip code.

One interesting finding is the seeming correlation between department and earnings. We (tentatively) found, by looking at the data, that Boston Police workers tend to be the highest paid city employees overall, with 44/50 of the highest paid workers being from that department. However, much of their earnings came from sources other than their regular pay, such as overtime, ‘other’, ‘detail’, and ‘quinn’.

We came up with three questions of the data, which are detailed below:

1. How is the budget earnings allocation per department? Where is the money spent on people? Even though we noticed Boston Police workers seemed to be the “better paid”, when we look closer at the dataset, we can see that the Boston earnings budget is spent on Public Schools employees with over $600M VS $345M for the Boston Police Department. One way to understand it is that the Public Schools budget is high because it has to pay a higher number of employees (over 50,000 people).
2. We were also curious about the relationship between the incomes and places of residency.  We conjectured that different income levels would contribute to where people choose to live; we would like to see the distribution of locations of residency grouped by the income levels.  The report provides us enough information to answer this question: total earnings and zip codes.  First we need to sort the data by income and group them into four income levels: low, low-middle, middle-high, high.  We need to have some context in order to set the breakpoints for these four categories. We realized that it would be helpful to have data on Massachusetts’s annual average or median income in 2013.  We were able to find the data by querying U.S. Census Bureau’s database. Using the data, we can establish the range for each category. Then, we can scatter-plot the distribution of each group on the map of Greater Boston Area.  The map can be easily found online, but we prefer to use python’s Basemap and Matlibplot libraries with the appropriate longitude and latitude to display the distribution.
3. Lastly, we were interested in visualizing the breakdown of the Boston Police employees’ wages, as much of their earnings were comprised of earnings outside of their regular pay. What percentage of their pay is due to overtime or other sources? Does this percentage vary by position? How do they compare? To accomplish this, we would take the data from all police employees, add up the numbers in each category, and produce a pie chart of the results. If we wished to break the numbers down further, we could separate the data by position and create a set of pie charts. All of this data can be sourced in the original data sheet.

Jia Zhang and Laura Perovich

The primary dataset we chose to investigate is the “School Gardens” (https://data.cityofboston.gov/Facilities/School-Gardens/cxb7-aa9j) dataset from the boston data portal. This dataset lists all schools in the Boston area that has a school garden.

 1. What is a school garden?(Rahul)

 We were wondering this ourselves. What does this dataset actually mean? It is helpful to created a visual dictionary of what school gardens are and for this we created a google map to zoom in on the different schools. It is hard to locate gardens, but what we found instead is that while the visual setting of these schools are diverse, we see a particular pattern. High schools are surrounded by parking spaces, middle schools by colorful markings on concrete.  With few exceptions, schools are enclosed buildings very much separated from the outside communities, they look protective. Some are even shaped so that buildings surround an inner outdoor play place.

A set of ground-level Images of the listed school gardens would further enhance the visual dictionary of school gardens.  These images could be requested from the schools, collected in person, or acquired online through school blogs, Google Image search, new articles, or Google street view. For example images of the Boston Latin School garden are available through a google images search leading to a press article:

http://blog.mass.gov/energy/education/boston-latin-school-honored-for-sustainability-health-environmental-education/

Without cleaning the data much, we made this preliminary map to better see the schools: http://www.mapcustomizer.com/map/schools%20with%20gardens

This map includes some obvious mistakes, but it is still very helpful for us to navigate and understand the data quickly. We have highlighted some of the interesting landscapes surrounding schools in the post itself.

Some interesting data/images(all from google maps):

At Boston Arts Academy, school gardens and community gardens are the most prominent in the school’s surroundings.

At other school garden locations, the larger environment makes the schools look more isolated. Overall schools are L or U shaped concrete buildings with playgrounds for middle and elementary schools, parking lots for high schools and a line of trees at the borders of the property.

 

Potentially we could analyze these images if we standardize the scale and zoom level to measure the percentage of greenery and gray concrete in each school’s environment. We could go beyond the idea of school gardens to address school settings in general.

2. Context – What % of schools have gardens and how do school gardens relate to other urban planting?

We saw right away that many of the schools on this list were elementary schools. We decided that it would helpful to get the context of school gardens by comparing our list to the list of public schools in Boston found at http://bostonpublicschools.org/.

We also thought it would be helpful to find how school gardens fit into the other urban planting around the city. Both community gardens (https://data.cityofboston.gov/Health/Community-Gardens/cr3i-jj7v) and urban orchards (https://data.cityofboston.gov/Health/Urban-Orchards/c7cz-29ak) are listed in the data portal and could provide context in how school gardens fit into the city’s greenery landscape.  Information on environmentally or ecologically focused businesses and non-profits in the area would also provide interesting contextual information.  A list of non-profits sorted by category can be found at

http://www.mass.gov/anf/docs/hrd/policies/leave/nonprofit/approved-nonprofit.pdf.

 

3. Is a school garden an useful indicator of quality of education in a school?

In order to see if school gardens are built in schools with particular economic and academic profiles, we felt that it is helpful to compare the garden locations with both standardized testing scores and income data for the areas the schools are located. Although standardized testing is a heavily disputed measurement of the quality of education, we felt that it did offer a reasonable comparison to the garden data we found because of its comprehensive coverage. MCAS results by school can be procured online at: http://profiles.doe.mass.edu/.  This site also provides detailed information on the student demographics (race, gender), class sizes, student to teacher ratio, and teacher qualifications.  It also sorts schools by type–public, charter, private, etc.  This brings up an interesting question as to whether school gardens are useful indicators of the type of education offered at a school; from an initial scan it seemed that a number of schools on the list are charter schools. 

Similarly with income data, we felt the coverage and standardization of the census data on area income could be a helpful complementary dataset. The American Fact Finder’s (http://factfinder.census.gov/) income data by houshold can be found by selecting by area, and then by category at it’s data portal.
These datasets, starting with school gardens, but expanding to school environments in general would be helpful in potentially determining whether there was a correlation between the quality of education, wealth, and the quality of the school environment.

Amy Yu & Ceri Riley

The primary dataset we looked at represents the locations of Children’s Feeding Programs in the Greater Boston area, ranging from after-school programs to those offered at community centers. According to the data (represented in this bar graph), there are only 19 total children’s feeding programs, many of which are concentrated in the Jamaica Plain region (4) and the Dorchester region (3).

From this dataset, we came up with three questions:

1) Does the availability of children’s feeding programs correlate with outcomes such as childhood obesity rates?

Because children’s feeding programs most likely do not have the budget or resources to distribute large amounts of healthy food, we wondered if there were any regional correlations between areas with more children’s feeding programs and outcomes related to child health.

For this question, we found a dataset based on a Google search – a .pdf report about The Status of Childhood Weight in Massachusetts, 2011. Because this report resulted from a BMI screening of public school students in Boston, we can correlate the overweight/obesity statistics from schools within a certain region with the presence of children’s feeding programs. In addition, we could directly look at the difference between body mass indexes of children in a public school with a feeding program, contrasted with those of children in a nearby public school without a feeding program.

2) How does the geographic distribution of feeding programs for children compare to the distribution of food insecure households? How does it correlate with household income?

Our original dataset is also a good starting point to investigate the class question of food security, so we decided to look for data on the economic stability and food security of the various regions in the Greater Boston area. We found these datasets by searching on Google and the Boston City Data Portal.

The Report on Hunger in Massachusetts is a .pdf generated by Project Bread in 2013 that presents Greater Boston-area incomes in relation to average food costs, both of which can be correlated with the locations of the children’s feeding programs. The Food Security in US Households .pdf report was released by the USDA in 2013 to present data on food security nationwide, and we can look specifically at the Massachusetts and possibly Boston statistics to find the most relevant data. The final two relevant datasets are a spreadsheet of Economic Characteristics by Neighborhood 2005-2009 and a .pdf of Boston in Context from 2007-2011, both showing the economic status of specific regions of Boston which correspond to some of the regions where there are children’s feeding programs.

3) How many children are these programs reaching? Is there missing data that should be considered?

We wondered whether these feeding programs are located in areas where there are many children, and/or if they especially targeted areas with children that might need extra care already, for example those that have working parents. By searching on Google and the City of Boston Data Portal, we found several relevant datasets.

To find out the number and distribution of children in the Greater Boston area, we found an excel spreadsheet with the 2010 Census Data for Boston and a corresponding .pdf report describing Boston By the Numbers, Children and where most children live (Jamaica Plain is 6th out of 10 regions and Dorchester is 1st with about 4 times the number of kids). In addition, we found an excel spreadsheet of the types and locations of Day Camps in Boston, where parents might drop off their kids with or without prepared meals, to compare with the feeding programs dataset. And we also found a excel spreadsheet of all the Boston Public Schools to see how the number and locations of feeding programs correspond to the number and locations of all the public schools.

Harihar Subramanyam & Danielle Man

We examined a number of datasets about police, shooting crimes crime, and emergency services in Boston. We primarily used the Crime Incident Reports dataset, which indicates the type and location of crimes in Boston. We cleaned the .csv data, separating the latitude and longitude into separate columns, with Python scripts.

We have three questions:

1. How is shooting crime distributed around Boston?
2. Do the locations of the police stations and hospitals make sense given the crime distribution?
3. How does police violence (especially towards minorities) in Boston compare to other countries?

Question 1: Crime Distribution

Let’s look at the shooting crime distribution over time and location.

Let’s plot the shooting crimes on the map.

We notice that shooting crimes are not small in number and that they are clustered in central Boston. Now let’s map shooting crimes by year.

It appears that the distribution has not changed much year to year.

Question 2: Police Stations and Hospitals

Now that we know where the shooting crimes are, let’s see if police stations and hospitals are optimally positioned to respond to them. To answer this question, we need more datasets. The Boston Police District Station and Hospitals Locations datasets give the names and locations of the Boston police stations and hospitals, respectively.

The map below shows that the hospitals (red) and police stations (blue) form a ring around the cluster of shooting crimes and are within one mile of almost every shooting crime.

Hospitals are red and police stations are blue. They form a ring around the crime cluster. See the website here.

Question 3: Police Violence

Finally, given that police violence is a growing concern in the U.S., let’s look at how Boston compares to other cities. Again, we need more data, so let’s look at Fatal Encounters.

Killings by state. For the full visualizations see here.

We notice that Massachusetts does not stand out compared to other states. Looking at counties shows that Boston has fewer killings than almost all other large cities – see here.

Finally, we focus on Boston and look at the number of killings based on race, gender, and symptoms of mental illness.

Notice that primarily men are killed, but that the distributions seem to be similar over the races.

Conclusion

We started with the crime incident dataset and combined it with other data (hospitals, police stations, and Fatal Encounters) to pose questions about crime distribution, police/hospital response, and police violence. With some visualizations, we explored the questions and discovered some interesting factoids. For example, hospitals and police stations form a ring around the cluster of crimes and police violence in Boston is not as extreme as in other cities.

Further exploration of these datasets, and perhaps other datasets, can help answer our questions.