A Quick Geographical Analysis of Homicides in Baltimore Before and During the Current Homicide Epidemic
There was a lot of police movement yesterday as I headed home. By the time I did get home, I found out via the news that a homicide detective had been shot in the head. His condition is very severe, and the prognosis is poor according to all reports. What a lot of people — from talking heads to politicians — are saying today is that “something” needs to be done about the violence in Baltimore. Indeed, homicides are at an all-time high in terms of per capita rates.
When you look at the rolling average number of homicides per day for the previous 365 days, you can clearly see when the acceleration started (around April 2015) and how it has not stopped:
The graph above is a little hard to understand so let me tell you how I created it. First, I took the number of homicides the year before any given day. So, for example, if there were 300 homicides from April 15, 2014, to April 15, 2015, then the number plotted is 300/365, or 0.82. Then, on April 16, 2015, I look at the number of homicides from April 16, 2014 to April 16, 2015. If there are no homicides on April 16, 2015, then the number plotted is 299/365, or 0.819… And so on each day.
As you can see, sometime around April of 2015, shortly before the Freddie Gray riots, this rolling average began to increase. It then increased sharply the rest of the year and into 2016. At one point, there were almost 365 homicides in the previous 365 days. Then there was a decline until early 2017, when the number climbs again. This rolling average eliminates the artificial boundary of date/time conventions in the calendar. Instead of comparing the homicides from, say, January to March of one year to the previous year, this average allows you to see the previous 365 days in context and without the calendar as a “bin” in which to classify the numbers.
Interestingly, but not surprisingly, the shooting happened a couple of blocks from an area of Baltimore that has seen a marked increase in the rate of homicide per 10,000 residents since the violence started to pick up in 2015. Before this shooting happened, I created a map using some publicly available data to look at what places in Baltimore had seen an increase or decrease in homicide rates since the epidemic began. (I’m defining the epidemic period as 2015 to the present. For the map, I defined it as 2015 and 2016.) The map is on the header to this blog post and linked below. It’s a big file, so I couldn’t fit it into the body of this blog post.
So here’s what I did…
- Take the list of homicides since 2005 provided to me by a Baltimore Sun reporter. The Baltimore Sun has been tracking these homicides since 2004, or so, and keeping close tabs on the outcomes of investigations. They have a very nice, accessible web page with the cases and mapping of their locations.
- I geocoded the list to make sure the addresses of the incidents were plotted correctly on a map. Since most addresses (about 99%) are at the block level, meaning that we only get the block of the location of the incident and not the actual address, there may be a slight error of a few meters on where the incident occurred. Still, this should not affect the results as very few incidents happened on a boundary line where I would have to make an arbitrary decision on where exactly the incident happened (which side of the line?).
- I then took information from the Baltimore Neighborhood Indicators Alliance (BNIA) and the US Census Bureau to look at the demographics — i.e. the population count — of the different Community Statistical Areas (CSAs). The BNIA uses CSAs instead of individual neighborhoods to promote more homogeneity in the area that they’re analyzing. For example, a neighborhood might overlap two census tracts, so it is easier to lump it with the rest of the neighborhoods on those tracts and get better numbers from the Census Bureau.
- I took the homicide counts per year in those CSAs and derived an average per year for the two time periods: the pre-epidemic period of 2005-2014 and the epidemic period of 2015-2016.
- I then took the population size for the CSAs and determined the average rate of homicides per CSA for the two time periods.
- I then subtracted the rate in the pre-epidemic period from the rate of the epidemic period, resulting in an absolute change in homicide rates per year per 10,000 residents.
- Finally, I color-coded the CSAs according to how much that rate changed between the two time periods.
Here are my results:
Out of 55 CSAs:
- 16 declined in the yearly homicide rate (Green on the map)
- 21 had an increase between 0 and 2.0 homicides per 10,000 residents (Cream on the map)
- 9 had an increase between 2.1 and 4.0 homicides per 10,000 residents (Yellow on the map)
- 8 had an increase between 4.1 and 8.0 homicides per 10,000 residents (Orange on the map)
- 1 CSA (“Poppleton/The Terraces/Hollins Market“) had the highest increase. (Red on the map)
- 2005-2014: the homicide rate was 5.31 homicides per 10,000 residents per year (3 homicides per year).
- 2015-2016: the homicide rate was 19.66, for a change of 14.35 homicides per 10,000 residents per year (10 homicides per year).
The shooting of the detective was two blocks north of the red area on the map, an area that has seen plenty of violence in the last few years. This morning, the Mayor, State’s Attorney, Police Chief and other authorities were all talking about how “something” needs to be done. This confuses me because it makes me think that nothing was being done in the previous days, months, or years where crime in the area has been very high… Or that those deaths and those shootings didn’t matter as much as the shooting of a detective. (Though, let’s face it, society places a higher value on the detective than, say, a teenage gang-banger.)
The map does give a ray of hope, though. The Cherry Hill CSA has historically been a violent place with plenty of gang activity and youths involved in violence, among other criminal activity. Between the two time periods I looked at, the average homicide rate per year dropped slightly. This is significant in that many other similar CSAs — similar in size, demographics, socioeconomics, etc. — saw an increase or no change between the two time periods. So something happened in Cherry Hill. Something worked.
What that something was is for another blog post at another time. For now, you have the results of a very quick and somewhat dirty (as I’m using not-so-clean data from news reports and not “clean” data from, say, the Maryland Violent Death Reporting System) analysis of the situation. There is still plenty of work to be done to fully understand what his happening and what is being done about it. Some of these things that are happening are happening at the individual level. Others are happening at the neighborhood level. And others at the institutional level. Whatever the interplay is, it’s costing a lot of lives.