Source: Rich Blocks, Small Blocks

* These data have high margins of error in small geographic units like Census tracts, which this service uses, so don’t take the figures literally. Still, the estimates can be useful for spotting broader trends about communities.

Thanks to the wife for sharing this discovery.

To start, I grabbed a simple test data set — five months of geocoded major crimes in D.C. from January to May this year — to check out some features. One I like is allowing users to query their data in the browser-based interface and filter for specific types of records.

Here, for example, I narrowed the map to show just thefts:

Assaults with deadly weapons:

Vehicle thefts:

Thefts from vehicles:

Robberies:

Homicides:

I made these maps in less than five minutes, so I’m sure there are much more useful stories to tell with the tool. There are also many, many features I didn’t explore, like the ability to style the map using Carto, the CSS-like language, rather than the UI.

Anyway, give it a shot, and let me know what you build.

Looking at the data, it doesn’t appear that car burglaries are on the rise in our larger neighborhood, Ward 5. Residents here reported 109 car burglaries in January, for example, but only 59 in April. There also doesn’t appear to be a week-to-week uptick in April. I wondered whether the time of year makes car burglaries more likely, so I downloaded six years of major crime data (170,000 incidents of murder, robbery, theft, car theft, arson, sexual abuse, and vehicle burglary) to find out.

This chart shows the six-year trend, by month, in the city for car burglaries. There has been an uptick in recent history during May, it seems. Incidents of the offense have then leveled off before spiking again in the fall:

Our ward has shown a somewhat similar trend:

Data source: data.dc.gov

There must be a mistake, right? I called the agency this morning and got a data dump of our daily meter readings (yes, they have the technology to capture this daily, but they have trouble moving customers).

Armed with the data, I built this interactive line chart, which shows our daily water usage in the last 97 days. Notice the spike in mid-March. The city says we used 30,000 gallons of water in a four-day period, or about 80 percent of our overall use since January. (We average about 420 gallons a day with these outliers. If you remove the spike, we used about 80 gallons per day).

Clearly, something went wrong:

See larger, interactive version made with Highcharts.

There’s a reason: Among the top 50 most-populous local governments, D.C. simply has more police officers per resident, according to the U.S. Department of Justice, which surveyed large police forces a few years ago. The city has about 670 cops per 100,000 residents, well ahead of Chicago, which was second with about 472 per 100,000. Houston had about 220, and Dallas had about 260.

Of course, D.C. is the capitol and diplomatic center of the country, and it’s densely populated with pockets of high crime and poverty. So a large officer to resident rate is understandable. But it’s a bit surprising how much D.C.’s ratio eclipses that of other major cities.

This chart shows the cities among the top 50 that have the highest per-resident officer ratio:

Here are the data for all 50 cities plotted on a map made with TileMill. Larger symbols represent higher numbers of officers per 100,000 residents:

See larger, interactive version

Data source: U.S. Bureau of Justice Statistics

The map used 2000 Census data to show how black residents are clustered in northeast and southeast neighborhoods, while white residents live in the northwest. He also mapped poverty, income, crime and education — creating a stunning series of images about inequality in the city.

I don’t have Rankin’s cartography skills, but I’ve tried my best to update his race map, using similar colors and features, with the 2010 Census data. First, this map shows concentrations of black residents, who made up roughly half the city’s population in 2010, down 10 percentage points from the previous decade: 

This map shows where Hispanic residents are clustered: 

Here’s another version with all major race/ethnicity groups. The dots represent 25 residents per U.S. Census block: 

All the data used to make the maps can be download here. 

This first chart shows monthly averages. As you can see, Austin experienced 100-degree average high temperatures in July and August (with little rain), setting the stage for the destructive wild fires spreading around the city. 

View larger, interactive version

Here’s a day-by-day comparison: 

View larger, interactive version

Data source: Weather Underground | Download: Days | Months

It has, though, endured three tropical cyclones, all of which were unnamed: 

The first, a tropical storm from 1933, crossed directly over American University in northwest DC:

The second, in 1939, was a tropical depression. It moved through southeast DC along the Anacostia River near what’s now RFK Stadium: 

The last, in 1945, was something called an extratiopical cyclone. It clipped southeast DC: 

It’s getting windy outside. I should save this post before we lose power…

Residents asked to have potholes filled, snow and ice removed and defective parking meters repaired, among more than 100 other request categories. 

These simple charts, made with Google’s image chart tools, show when and where residents made those requests:

* Ward 2’s figures are inflated by more than 100,000 requests to repair or disable parking meters. This could be because these neighborhoods have the highest population density in town, or because there’s heavy construction that requires meters to be turned off temporarily, or some other factor. 

Each requested is geocoded with latitude and longitude, so look for some maps soon…