The great news is that analytical capabilities previously limited to larger companies with deep pockets are now available to a broader range of organizations. Plus, even larger companies are benefiting from new SaaS and cloud-based technologies that allow easier access to data often locked in legacy systems. More and more companies can therefore take a proactive approach to their decision making, an approach based on facts, vs the reactive approaches of the past.

Police report

Take law enforcement. When SpatialKey first launched, we immediately received interest from police departments looking into better approaches to crime fighting. Most already used sophisticated RMS or CompStat data systems, yet those systems did not take into account data from other systems or outside sources. For example gang data might be in one database, crime incidents in RMS, and offender information in a spreadsheet with no easy way to pull the information together to make better policing decisions. What they saw in SpatialKey was a way to get a better ROI on their investments by adding new dimensions to their data analysis capabilities.

With SpatialKey, a commander for example can bring in data from multiple sources and start looking for patterns: there are a string of robberies happening in a neighborhood- how can we curtail these? Who are the parolees or known offenders living within x miles of that area? Is there a specific time of the day that these crimes occur? In the police world, they call this Intelligence-led policing- the ability to leverage intelligence to guide crime fighting decisions. So if the commander sees a pattern in the time when crimes occur, increasing the number of patrol cars in the neighborhood at that time is a fact-based approach that hopefully will generate faster/cheaper results. And all it took was a couple minutes of analysis with SpatialKey vs randomly patrolling the area.

Well data is data is data. What we’ve seen in the past year is that a Sales VPs’ data analysis needs are actually not that different from a police chief’s (or any other decision maker for that matter). At the end of the day, what’s important for organizations is to find ways to pull together information critical to decision making and make it available to the people who need to actually make those decisions. So for one parolees, crime logs, or calls for service are key data points to look into to make fact-based decisions, for the other it might be sales leads, product sales, customer calls or customer demographics. And of course the types of data SpatialKey clients need to analyze is as varied as their industries.

The common thread we’ve found amongst all of our clients is that although they already have data analysis capabilities (most already use some form of BI or GIS system), those systems do not provide them the ad-hoc, “get the answer now” data analysis needs the addition of SpatialKey provides.

So although a Sales VP might be using Salesforce as a great CRM tool, there is no easy way to tie the salesforce data to other internal or external data sources to make better decisions to maximize sales opportunities. By incorporating SpatialKey with Salesforce, he/she can now bring in demographic information or competitive data into the picture. Are there patterns, geographic or otherwise, that can help guide sales staff deployment or lead closing activities? Is there a correlation between deal size and marketing activities? What works best to close leads in this geography vs the other?

In a fast-paced world when we all need an answer yesterday, the benefit they see in SpatialKey is that not only can they leverage knowledge they never had before, but they also don’t need to wait on data analysis gurus to get answers. They can do that analysis themselves- on the fly. Many actually use SpatialKey in meetings so that “what if” questions can be answered immediately. Maybe we should call this Intelligence-led sales…..

Please visit spatialkey.com for more information. We also offer a free trial if you want to take a spin of the app with your own data.

Analyzing conflicts via maps is not new. For centuries generals and politicians have moved pushpins on maps to help guide troop movements, understand enemy positions, or help avert conflict altogether. What’s new to conflict and crisis mapping are the tools now allowing military, politicians, and humanitarian groups better understand what drives political instability and violent conflict, and better address it.

One of the more rewarding uses of SpatialKey has been driven by a team led by Dr Clionadh Raleigh. Dr Raleigh and team members from the PRIO Center for the Study of Civil War created the ACLED (Armed Conflict Location and Event Data) database. The ACLED project team’s objective was to provide a better read of conflicts by understanding the relationships between combatants, social groups, economies, and even natural phenomena such as droughts or floods. Ultimately their goal is to achieve a more stable, just, and peaceful world.

Crisis mapping

Thanks in part to funding from the World Bank, ACLED provides up-to-date, immediately accessible analytics and maps for over 50 countries in the developing world to help academics, the World Bank, NGOs, aid agencies and more gain insights on civil war dynamics. The database includes for example the date and location of conflict events, event types, rebel and other groups involved, as well as specifics on battles, killings, riots, and recruitment activities by rebels, governments, militias, armed groups, protesters and civilians, and much more.

The difficulty of creating a central database for crisis mapping is that it needs to bring together vast amounts of diverse information coming from a wide variety of sources. In technical terms: a data mess. Since neither Dr Raleigh nor the users of the ACLED database are trained GIS (Geographic Information System) professionals, they decided to use SpatialKey to centralize and analyze the data. SpatialKey is web-based and does not require special training or programming. All users need is an internet connection to immediately create highly visual maps and reports.

The benefit of using SpatialKey is that each agency using ACLED data can now create maps to help answer totally different questions, no data specialists required. Some groups need to better understand how to mitigate conflict in a specific area, others want to find the safest zone to place a refugee camp, and yet others want to understand the impact of possible floods and droughts on a conflict so they can arrange their resources accordingly.

This has allowed researchers to analyze data with more precision, as well as create a more collaborative environment to help the researcher community create predictive models of civil war. It has also helped challenge assumptions. For example Dr Raleigh says that many people considered civil wars to be primarily rural events, but SpatialKey has showed that these conflicts tend to happen close to larger cities, as rebel groups attempt to engage with the military. She considers that the combination of ACLED and SpatialKey goes a long way toward advancing the field- it provides the next generation in conflict analysis and crisis mapping.

If you cannot attend Doug’s session, please read our case study on the use of SpatialKey by ACLED.

For more information on SpatialKey, or to start your free trial, please go to spatialkey.com.

SpatialKey as a visual dashboard is a great example to highlight since it’s commonly used by our clients- but since our clients don’t really want to share their company data with the world for us to use in demos, we thought we’d use ours! Of course, we changed employee names and client revenues for this video since we don’t want to reveal our secrets either.

As a background, Universal Mind, SpatialKey’s parent company, is a leader in the field of Rich Internet Applications (RIA) development using next generation Web 2.0 technologies. The company is comprised of employees, as well as a network of consultants, all spread throughout the US. Clients include companies as diverse as Adobe, Cisco, Behr, CVS, Ben&Jerry’s, Visa, Children’s Hospital Boston, Verizon and many more. Dispersed teams often don’t get the big picture of how their work fits within the broader company or how they are performing compared to others. For Universal Mind, SpatialKey has helped bridge that gap.

Tom’s presentation focused on sales activities and sales performance. How did we do last year? How did we grow geographically? Which clients generated the most aggregated revenue? What trends do we see for the year ahead? Are there any location trends we should capitalize on? Who were our top performing sales reps? Is there a correlation between the location of our reps and the success of our sales activities? Which consultants generated the most billable hours? Where are they located? What revenue did they generate across clients?

See the video above for a small example of the many visualizations and analysis capabilities SpatialKey offers for sales data. Notice how quickly Tom gets answers to any question he asks about the companies’ sales activities. Universal Mind uses Salesforce to track and manage its sales pipeline, so the data you see highlighted comes from a direct import from Salesforce, but any CSV-type data would work just as well.

If you are interested in finding out more or need help setting up your free trial, do not hesitate to contact us.

That’s the business SpatialKey is in. We’re allowing people who in the past may not have been on the front lines of data analysis to take their spreadsheets, CRM data, salesforce.com data and more, map it, analyze it via simple click and drag tools, and extract meaning from it. Our goal is to help people -regular business people, not just data specialists- ask questions of their data, understand trends, and make better decisions, faster.

Police report

The great advantage of working at SpatialKey is that our clients come from such a diverse variety of organizations- each with totally different problems to address, and data analysis needs. We’re helping agencies better understand war zones and address conflict, allowing pharmaceutical companies analyze vaccine distribution efficiencies, or sales departments gain deeper insights into their sales funnel. Our partner Social Compact and the Citi Foundation recently launched a new service based on SpatialKey to help drive investments to undeserved communities. We’re even helping police departments get more bad guys off the streets by allowing them to better understand and address crime patters. The list goes on. Right now for example,  I’m working on a case study with a client in the energy sector. They are a publicly traded company that buys excess electricity from schools, businesses, etc and sells it back to utilities or grid operators when energy use spikes. In their case, they use SpatialKey to better monitor and analyze energy capacity and therefore more quickly respond to energy need changes. SpatialKey provides them insight that they could only dream of in the past. All that with no programming, nor expensive hardware, training or software investment.

Marketing team report

The common thread among all our clients is that they have tons of data, most of it coming from a variety of sources. They know that they could do a better job putting the pieces of the puzzle together to make better decisions, but they don’t have the energy nor budget to invest in complicated tools. Each one of our clients were amazed when they saw their first demo of SpatialKey. They suddenly saw their data like never before and could not believe such a simple solution had so much power. The power they needed to drill down into their data and make better decisions.

Crisis mapping

The other thread we’re seeing is that once SpatialKey is used within an organization, it does not take long for others within the same company to want to start using it too. Take the example of our energy client. They immediately saw that they could use SpatialKey to track not only kilowatts, but also leads, prospects and more.

Our engineering team is hard at work developing new functionalities into SpatialKey. We all know that the future is about more data, not less, so we want to provide the tools needed to allow our users to see the pins in the haystack that will help guide their decisions. Looking forward to seeing what other client uses are coming our way. Want to find out more? Go to Spatialkey.com or contact us.

Photo courtesy of CNN and Google maps.

On the business side, 80% of business data has a location component which provides a goldmine of untapped information for marketing, sales and operations. But current visual mapping and analysis tools are expensive, can only be accessed by trained specialists, and require heavy IT involvement to set up and maintain. This is a big barrier to entry for most businesses. They want to “see”, understand and communicate data trends, but don’t have the time nor means to invest in yet another expensive infrastructure.

The businesses that already do leverage visual mapping and analysis can more effectively and more quickly see geographic or time-based data and trends critical to sales and operations. This provides them a real competitive advantage. Many oil and gas companies for example have invested in sophisticated Geographic Information Systems (GIS) and brought in GIS specialists to gain insight on their location intelligence via visual maps. This allows them not only to plot areas with the highest potential to drill in, but also better manage their pipelines, operations, retail facilities, and more.

…. or….    . …and…            … and..     

Thankfully, a revolution is taking place that allows “regular” business users -with no GIS training nor deep pockets-  to leverage the power of visual mapping and analysis. Enter Software as a Service (SaaS). SaaS is transforming mapping and data visualization in the business world the same way Google Maps revolutionized mapping for consumers. Using cost-effective, user friendly SaaS mapping and analysis applications, such as SpatialKey, organizations of all types and sizes can now import their business data, combine it with geographic or competitive information, and start visually analyzing trends critical to their business. Where are key customers located? How can they maximize results in their sales territories? How best to map their sales territories? Where should they open a new retail outlet? How does Q2 sales compare to Q1 on a geographic basis? What marketing campaign resulted in the highest ROI? And so much more.

Opportunities and threats previously hidden within row and column-based datasets are now clearly visible via interactive maps. Concepts difficult to explain in text or PowerPoint presentations can now also be shown and therefore easily understood resulting in better decision making. What’s more, since everyday decision makers can use these applications, “what if” questions can be answered on the fly versus having to wait for an analyst to do a new data query. Decision-making, communication, and collaboration are improved. After all, seeing is understanding and believing, even in the business world.

Note: we’ll be adding blog posts around visual mapping for sales and marketing users over the next few weeks. In the meantime you can find out more at our sales and marketing and/or enterprise solutions pages.

The first few rows of the Sacramento dispatch export

It took just a couple of minutes to import one of these files into SpatialKey, where we can produce rich interactive maps and reports related to dispatch activity in Sacramento.  The file from August 2009 contained about 30,600 records with location information.  Dispatches for Disturbance-Noise were the 7th most common type of dispatch in August.

Heatmap of the 1079 Noise Disturbances in Sacramento during August 2009.

By using the Temporal Heat Index and Timeline to inspect the date and time of occurrence, we get a better picture of when Disturbance-Noise calls occurred.  The Temporal Heat Index summarizes the number of crimes by hour of day and day of week.  Notice the dispatch volume is generally highest late at night – especially on Saturday and Sunday.

Most dispatches related to noise happen late at night on weekends.

So where are these early morning disturbances?  Simply select day/hour grids of interest and zoom in to see the detail.  Here’s a look at noise disturbances southeast of Capitol Park between 1 and 3am on weekends in August:

Southeast of Capital Park might be a good place to party but a hard place to sleep on weekends.

Try it for yourself

You can open up the sample report that we created to visualize these dispatches.  The report is fully interactive, so you can really explore the dispatch activity in Sacramento.  We saved the report with a filter for Disturbance-Noise. Try modifying this filter – and adding others – to see how SpatialKey works.  Interested in seeing where and when the 338 Drunk Suspects were encountered?  Or the 27 Shooting into Inhabited Dwellings?  Maybe you want to see where and when the 2246 Subject Stops occurred.  It’s simple with SpatialKey.

• Try Sample Report

Looking for easy-to-use location intelligence from your own data?  Get started with our free trial, and start visualizing your data in minutes without installing any software.

Notes

The images and reports in this post were created with publicly accessible data.  Check out Sacramento’s dispatch page to see their notes about what data is included.  We omitted data without address location in our SpatialKey report.

We have no association with the Sacramento police department (but we’d love to, so contact us if you’re from the Sacramento PD and want to use SpatialKey). SpatialKey does have specific features designed for Law Enforcement.

While at ICCM2009, we’ll be discussing some of our work with ACLED (Armed Conflict Location and Events Dataset).  The ACLED project was founded by Dr. Clionadh Raleigh and team members from the PRIO Centre for the Study of Civil War, who set out to create a conflict database that would help answer researchers’ questions.  ACLED was subsequently funded by the World Bank with the aim of better understanding events in client states.

This video gives a brief overview of how SpatialKey is being used to investigate the relationship between this data and the location of refugee camps and environmental factors. What’s significant is that SpatialKey is making it easier for ACLED researchers to collaboratively bring together vast amounts of information from a wide variety of sources in a meaningful way – all without having to involve programmers or GIS experts to get their work done.

A recent Reuters article about trends in crisis mapping highlights the very issue SpatialKey is helping researchers solve:

Researchers have used maps to visualise crises for many years.

But there are drawbacks in the the use of highly-sophisticated, computerised Geographical Information Systems (GIS), which are usually used in such work — not least that they are expensive and difficult to operate.

Nor do these systems allow for much integration and collaboration, and due to their complexities they are not usually updated in real time.

For more details on our work with ACLED, read the ACLED crisis mapping case study.

How much further will Saturn customers need to travel to get to a GM dealer?  Not far, based on a location analysis of Saturn and GM dealerships.  Every one of the 386 Saturn dealerships are within 10 miles of an existing GM dealership, according to a SpatialKey analysis of data from AggData.

Beyond being a powerful tool for decision-making, location intelligence can aid in customer service, customer outreach, and public relations.  Did GM employ location analysis to determine that every Saturn dealer is nearby a GM dealer?  We’re not sure, but it might have been helpful to include this thoughtful message in their press release.

A map visualization, produced by SpatialKey, showing all GM dealerships (heatmap) and Saturn locations (graduated circles).

(Click images for larger versions.)

Florida has a particularly high concentration of Saturn dealerships (in blue) relative to GM dealerships (in green).

Of all Saturn dealerships, Saturn of Nashua is furthest (as the crow flies) from a GM dealership. Even still, the nearest GM dealership is only about 8 miles away.

To learn more about proximity filtering in SpatialKey, see our demonstration of finding drug crimes near schools or proximity filtering in the feature tour.

The following video walks through how quickly we were able to use SpatialKey to filter down over 14,000 crime records from the past 90 days to isolate these exact drug crimes. By the end of this short 5-minute video we produce a list of the schools that have these particular narcotics incidents near them. This is an example of using the powerful proximity filtering in SpatialKey to filter one dataset by buffering around another, as well as filtering by type of crime, day of week, and hour of day.

Data sources

Both the crime data and the school locations were downloaded from San Francisco’s new data warehousing website: datasf.org. The police department provides a feed of the last 90 days of crime incidents as a shapefile. The dataset used in the video was downloaded on September 21, 2009 and includes crime records from June 25, 2009 to September 20, 2009. Additionally, the city’s GIS department provides the files for the locations of all the private and public schools in San Francisco (both as shapes and as points).

How we did it: step-by-step

I’ll walk through a visual explanation of how we filtered down the 14,653 crimes. These steps are the ones you see performed in the video. You can click each screenshot thumbnail on the right to view the full screenshot.

Step 1: Load in the crime data

We imported the shapefile that we downloaded from the SFPD’s website into SpatialKey and opened a new full-screen map template. This initial map shows all 14,653 incidents. The heatmap shows the distribution of crimes in the city. It is very clear that the most crime activity occurs downtown.

Step 2: Buffer around the schools

We then brought in our second dataset, which was the shapes of all the public and private schools in San Francisco. We used the Proximity Filter capabilities of SpatialKey to create a 1,000 foot buffer around each school and only include the crimes within that buffer. You can see the filter in action and how it removed all the crimes that are not near schools. This filtered out approximately 6,000 crimes and brought our total count down to 8,417.

Step 3: Only show drug crimes

Since we were only interested in narcotics crimes, we opened up a pod to show the type of crime (this was a field called “Category” in the data provided by the SFPD). We filtered out all crimes except “Drug/Narcotics,” which left only 1,849 crimes.

Step 4: Filter by day of week and hour of day

Then we filtered by Day of Week to only include crimes occurring Monday through Friday. We also opened up a histogram showing the distribution by hour of day and we selected a range from 7 am to 4 pm. This is an approximation of school hours. Obviously all schools will have slightly different exact hours of operation.

Step 5: Only show heroin/crack/meth

The Juvenile Drug Trafficking and Schoolyard Act is specifically for certain types of drug crimes, so we needed to filter out the other drug crimes that we weren’t interested in. Luckily, the SFPD provides a field in the dataset called “Description” that provides more granular detail about the type of crime, including what type of drug was involved. We filtered out any crimes that did not include the sale of heroin, crack cocaine, and methamphetamine. This brought our total dataset down to 166 crimes.

So in literally under 4 minutes we were able to go from over 14,000 crimes to a very targeted list of 166 and from 243 schools down to a list of 21 that we would want to act on.

Try it for yourself

You can open up the sample report that we created to visualize these crimes. We saved the report with all the filters in place (buffered around schools, during school hours, only certain drug crimes). You can modify these filters if you want to see how the filtering in SpatialKey works. The report is fully interactive, so you can really explore the crime activity in San Francisco.

• Try Sample Report

Notes

This data included crimes during the summer when it is likely that most schools are not in session. The purpose of this example is not to find the exact crimes that occurred during the actual school hours, but instead to find the geographic locations where crimes tend to occur during school hours. The areas we highlighted tend to have drug crimes occur at the right time of day, so we are extrapolating that during the school year this trend would continue.

It is also important to note that this is data of active police activity (ie arrests, citations). This means it is not a comprehensive dataset of where crime occurs. This is data about where the SFPD is arresting people. This is an important distinction to keep in mind. An increase in the number of arrests in a certain area may very well be a very good thing indicating that the police are focusing on certain problematic areas, not necessarily that actual crime in those areas is on the rise.

The video, images, and reports in this post were created with publicly accessible data. We have no association with the San Francisco police department (but we’d love to, so contact us if you’re from the SFPD and want to use SpatialKey).

The growth of Wal-Mart provides a particularly compelling use case for the SpatialKey Animation Template.  Though most know of Wal-Mart’s southern provenance (Sam Walton opened the first Wal-Mart in Rogers, Arkansas, in 1962), the chain’s subsequent spread and resulting dominance over American retail is a more complex phenomenon.  Utilizing a dataset of over 3000 Wal-Mart store openings, from 1962 to 2005, our animation template shows the radial spread, increasing density, and overall coverage of the retail giant.

Launch this dataset in the animation template

The dataset used in our animation template comes from an economics paper, “Diffusion of Wal-Mart and Economies of Density” by Thomas J. Holmes, and is freely available here.  As Holmes notes,

Wal-Mart started in a relatively central spot in the country (near Bentonville, Arkansas) and store openings radiated from the inside out. Wal-Mart never jumped to some far off location to later fill in the area in  between. With the exception of store number one at the very beginning, Wal-Mart always placed new stores close to where they already had store density.

This pattern is clearly visible in our cumulative animation – a playback mode in which points accumulate over time on the map.  Switching the animation style to noncumulative allows you to select a decade at a time (or any time range), which can then be played over the timeline.  Highlighting only the store openings in the 1990s reveals a strategy aimed at the Northeast and California, with relatively few openings in the rest of the country.  Though this differs markedly from the early history of Wal-Mart, it is much more in line with the population centers of the United States, and reveals a company no longer rooted in the South.

To demonstrate some of the additional filtering and aggregating capabilities of our geovisualization toolset, the same Wal-Mart dataset can be visualized in our Drill Down template, which allows filtering by map extent, time, type, or list. Filtering by type allows you to show only the stores that have been converted to, or have always been, Supercenters. As noted by Holmes,

With this [Supercenter] format, Wal-Mart added a full-line grocery store alongside the general merchandise of a traditional Wal-Mart. Again, the diffusion of the Supercenter format began at the center and radiated from the inside out.

Thus, the spatial trend of Supercenter distributions appears about a decade behind the trend when all stores are included. Is this because Supercenters are largely made up of converted regular Wal-Marts? Or because shoppers in the South are more amenable to grocery shopping at Wal-Marts than shoppers in the rest of the country, where the presence of Wal-Mart is still somewhat novel? We’re not sure. But visualization is often as much about spurring questions as answering them. This dataset has been mapped before (see here and here), but doing so in a flexible geovisualization environment reveals interesting geographic and temporal patterns, only a few of which have been explored in this post.