Tag Archives: HunchLab

HunchLab – New Functionality, Two Videos and a Great Partner

Fueled by coffee and ice pops, the Law Enforcement team has been busy this year. We have been awarded a National Science Foundation Small Business Innovation Research Phase IIb grant to continue the development of new functionality, attended conferences and conventions and started working with a great partner, Jerry Ratcliffe from Temple’s Department of Criminal Justice .

Earlier this year, Robert Cheetham gave a presentation on HunchLab, our web-based geographic crime visualization, early warning and risk forecasting application at the Space Time Modeling and Analysis workshop as part of Redlands GIS Week.


Other presentations from the conference can be found here.

We have extended our hot spot/kernel density tool to allow for the animation of the maps to see how the density shifts through time.

With our NSF SBIR Phase IIB, we are working on different risk forecasting tools. The first tool that we are building in collaboration with Jerry Ratcliffe is a web-based near repeat analysis and visualization tool.

Near Repeat UI

While collecting links for this post, I stumbled across this video of Jerry and Little Nellie.

Mashing up Google Calendar and a Javascript Timeline

Usually, this blog is about geography and Azavea’s work, but I thought an internal project might be of interest to others.  Our marketing team recently faced an interesting problem.  Our marketing approach is not based on advertising. Rather, we focus on spreading the word about our work by performing presentations at conferences, writing articles, writing book chapters, our newsletter, etc.  We also respond to a fair number of RFP’s and grant solicitations.  As our marketing and business development team has grown, the number of activities to track has also increased.  Lots of activities also creates opportunities, but if we can’t effectively visualize how they all fit together, we run the risk of missing those opportunities.  In addition, the task of tracking all of the grant and proposal deadlines, conference attendance and other activities becomes pretty tough.

So we resolved to set up a shared calendar as a mechanism for collectively tracking all of these deadlines and activities.  We had  switched our e-mail system to GoogleApps Premium in early 2008.  When we did this, we gained a number of capabilities in addition to e-mail including: shared calendars, document authoring/storage and customizable home pages for each staff person.  So our starting point was to create a Google Calendar for the marketing folks to share.  However, many of the marketing and business development activities span several days, and while Google Calendar is a great way to enter and store events, the usual daily/weekly/monthly calendar layout does not make it easy to see several weeks or months together.  We were really looking for a ‘timeline’ display of the calendar so we would be able to see the juxtaposition of several events and their relationship to each other.  So we looked around for a low-cost system that would enable us to both enter our marketing activities and visualize them in a timeline layout.  We looked at online project management tools, some of which support Gantt charts, but while a Gantt chart is great for decomposing tasks into subtasks, it arranges each task into it’s own line.  So if you have 20 tasks, that’s ok, but if you have 100 or 200 spread out over a year, it’s not very readable – the chart just keeps growing vertically.

marketing timeline calendar

So we decided to build something in-house.  When we had first set up our wiki, David Zwarg had showed off a tool called Simile Timeline, created by some folks at MIT.  So we went back to that project and learned that not only had it continued to develop but it was available as an open source toolkit that could be used in a broad range of applications.  David picked up Simile and within a couple of days, he had mashed up 6 calendars within the account we’d set up for the marketing crew into a timeline-based calendar.  He also experimented with incorporating a map, but we decided it consumed too much screen real estate and nixed it.  After all, we’re still small enough that we generally know what part of the country every is in. :-)

While geography proved to not be very compelling for this application, the juxtaposition of space and time can be a very useful visualization.  Below are a couple of screenshots from one of the recent builds of of our HunchLab product (it’s used for forecasting and geographic change detection), where there’s a critical need to view both spatial and temporal patterns in the same view.

Figure 1: The points on the map represent the span of time selected on the graph with a heat map of the points.

Figure 1: The points on the map represent the span of time selected on the graph with a heat map of the points.

Figure 1: The graph below the map is a Time-of-Day/Day-of-Week graph, showing a "temporal heat map" of when the events in the map occured.

Figure 2: The graph below the map is a Time-of-Day/Day-of-Week graph, showing a "temporal heat map" of when the events in the map occurred.

David and Josh on Video

We had a busy autumn at conferences. Josh Marcus represented us at the first International Crisis Mapping Conference in Cleveland, Ohio.  He presented our work with HunchLab, the crime analysis, early warning and forecasting system we have been developing with support from the National Science Foundation.

Over the past year, David Zwarg has been devoting his 10% research time to supporting the mapping components on the SourceMap project at the MIT Media Lab.  He had a chance to present at the Boston Ignite Spatial a couple of weeks ago.  Check out his presentation on this video.

Subterranean Heat Map is Not What You Think

Our HunchLab team has been working on some new server-based kernel density routines that will generate density maps based on crime events.  Many in the GIS world have taken to calling density maps like these “hot spot” maps or “heat” maps.  But the recent map published by Transport for London is a little different – it literally shows which line segments have the highest temperature.  The tunnels through which the subways run have been steadily warming for the last century, with temperatures now exceeding 32 degrees Celsius and no air conditioned cars.  Some of them will get new air-conditioned cars in 2010, other lines with deep tunnels have no space for waste heat and are experimenting with alternative approaches to cooling the passengers.  These are static maps, but I think we’ll all be carrying temperature, noise and other sensors built into our phones and tablets in a few years.  That’s going to make for an avalanche of data, but some potentially fascinating applications.