With SpatialKey’s SaaS platform, insurers can now finally bring together and analyze *on-the-fly* – not in hours or days as with other tools- a variety of data coming from different sources, and make immediate business decisions accordingly.

Take tropical storm Alex, expected to turn into a hurricane (thankfully heading away from the Gulf of Mexico oil spill) this Wednesday as an example. Since some of the predictive hurricane models are proprietary, we decided to use publicly available datasets of the hurricane’s path at http://www.hurricanezone.net/#01l, as well as a mock sample of insurance policy to showcase how easy it was to import and analyze information using SpatialKey.

After downloading a shapefile containing the Tropical Storm ALEX 5-Day Track here, we easily imported it into SpatialKey and created a new report showing the potential 72 and 120 hour paths of the storm.

Tropical Storm ALEX 5-Day Track, Uncertainty Cone

Next we added our fictitious insurance company’s policy data and overlayed it with the predictive hurricane’s path. This allowed us to see in minutes, not days or hours, which policies in which geographies might be affected by Alex. Minutes vs days make a big difference- the quicker the information gets in the hands of decision makers within the insurance company, the quicker they can adjust their plans- for example where to dispatch local agents after a natural disaster.

Tropical Storm ALEX 5-Day Track with Policy files

SpatialKey shines by making complex data analysis simple and available to the people who need it the most.

Tropical Storm ALEX 5-Day Track with Policy files filtered

Within just a few minutes, no programming or analysts required, we imported the insurance policy data from a spreadsheet, shape files from the NOAA site and used the capabilities of SpatialKey to filter which policies could potentially be affected by the path of the storm. We could take this analysis further and forecast the impact of the storm on commercial vs home policies or per construction type. We could even import additional datasets, for example local demographics, for further insight. The analysis capabilities are endless. And the other benefit of SpatialKey is that the information (for example which policies are at the highest risk) can easily then be exported out of SpatialKey and shared within the organization for follow up. Or interactive reports containing the information above can be shared so that others on the team can further slice and dice it according to their analysis needs.

To find out how SpatialKey can help your insurance company, please visit here or contact us.

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.

CDE's SchoolVIEW

SchoolVIEW data in SpatialKey

I was interested in seeing the correlation between these different metrics, and (since we’re obsessed with location) how that correlation relates to geography. So, I imported that data into SpatialKey.  The source file was a CSV with a row for each school.  Here’s what that data looks like:

SpatialKey’s bivariate renderer allowed me to quickly explore the data in just that manner. The bivariate renderer allows you to select two numeric attributes in your dataset, and an aggregate calculation for each. In the image below, I selected average math growth percentile and average math proficiency.  Each dot in the scatterplot legend at the upper right represents a colored location (grid cell) on the map.  The position of the dot represents its relative score for average math growth (y axis) and average math proficiency (x axis).  The color “behind” the each dot is the color used for the corresponding grid cell on the map.

This visualization shows the coorelation between math proficiency and growth, as it relates to location. (Click the image for a larger view.)

We can see there is a general positive correlation, where most locations have a similar relative score for math performance and growth: Most points on the scatterplot are along an imaginary diagonal line from the bottom left (low in both metrics) to upper right (high in both metrics).  What’s often interesting and informative is to see areas that deviate from the norm in terms of the correlation.  Areas with relatively high proficiency but low growth – “strong but losing ground” – are colored blue, while areas with low proficiency but high growth – “risin’ up” – are colored red.  These are both negative correlations.  Areas that score low on both metrics are shaded white, while those high in both attributes are shaded black – both positive correlations.  For this type of visual analysis, areas that fall toward the middle of both ranges are usually less interesting, and so those colors are more transparent to allow you to focus on the extremes.  It may take a few seconds to orient yourself to this view, but once acclimated it’s a powerful way to visualize some complex – and otherwise difficult to express – relationships.

You can correlate any pair of attributes by simply selecting from one of the axes in the scatterplot legend. This next image compares average math and reading proficiency.  First, notice there seems to be an even stronger correlation between these two variables than the previous set.  (The points line up even closer on the imaginary diagonal line.)   It’s also interesting to compare these two images; Notice how the schools in some locations are relatively strong (shaded black) or weak (shaded white) in both visualizations, while others show a particular weakness in one of the metrics.

Selecting a point on the scatterplot shows the corresponding location on the map. In this case, we've highlighted a school that is an outlier because it's relatively strong in math versus its perforrmance in reading, realtive to other schools. We can easily see this school is in Moffat County. (Click the image for a larger view.)

SpatialKey makes it easy to uncover and visualize these relationships, and to share them with others. From uploading the spreadsheet with school data to presentation, this only took a few minutes to create – without any programming or hassle. And, this is just the start. By adding filters we can see these trends for schools of certain sizes or types, or compare these trends over time.

Further Analysis

An interesting next step would be to see if there is any correlation between the areas that deviate from the norm school performance and property value changes. For example, are the “rising up” areas ones where real estate values have been growing faster than average, or gentrification is taking place. (Of course, determining causality is a whole different conversation!) One could bring additional real estate or demographic data into SpatialKey to help answer those questions. SpatialKey makes it easier to understand the relationships between disparate datasets.

Try it out for yourself

Don’t take our word for it. You can start uploading your own data and visually correlating it right away by signing up for the 30-day trial of SpatialKey. Or, contact us and we’ll be happy to walk you through the process.

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.

Find some data

Your data must have location details down to the level of granularity that you are trying to map. For instance, if you want to show a map of states, all the records in your data should at least have US state (your data can be more granular too, you can map address-level data by state if you want). In this example I’ll be mapping US unemployment rate. The data for unemployment is provided by the Bureau of Labor Statistics and can be found here. I took the latest stats by US county and extracted only the data for October 2009.

After just a little massaging in a spreadsheet program my data looked like this:

You can download the CSV file that I used if you’d like to try it out for yourself.

Upload to SpatialKey

Once you have your data ready, you can upload it to SpatialKey. If you don’t already have a SpatialKey account, you can sign up for the free 30-day trial to get access right away and start uploading. During the upload process you’ll be asked to identify the location columns in your data, like street address, zip code, etc. We’ll do our best to automatically identify these columns based on your data, but you might have to help us out.

Make your map

When you load your data onto a map you’ll be asked what kind of map you want to create. We’ll make a thematic shape map, and we’ll choose to map the data by US geography (this includes state, county, or zip code).

Then we choose what the map should display. In this example we want to show the unemployment rate, so I’ll pick average unemployment rate, which will start me off with  a map of the US states with the average unemployment rate for all the counties in each state.

Now our thematic map shows the average unemployment rate for all the counties aggregated by state.

I can switch between this state view of the data and counties by changing the options in the layer’s settings panel.

Now we have a map of all the counties in the US (including Puerto Rico) that shows the unemployment rate of each county.

Customize and Explore

You can easily customize the bin ranges if you want to tweak them, or you can control the colors used (all maps are the same, just with a different color scheme):

You can also use all the filtering options that SpatialKey offers to filter the data in your thematic maps. Here’s an example of filtering to only show counties where the total labor force is over 100,000.

And here’s another example to show only the counties where the unemployment rate is greater than 15%:

No Programming Required

To generate these maps you don’t have to write a single line of code. It’s as simple as uploading your data and stepping through a few guided steps. If you wanted to change the map to show the total labor force per county instead of the unemployment rate, it only takes 3 clicks. There are lots of ways to make these maps, like this great tutorial on FlowingData shows, but we think SpatialKey gives you the easiest way to create and analyze thematic maps.

We mapped the concentration of where these permits were issued. The different types of buildings, such as apartments versus office buildings, have very different distributions throughout the city. Some of these distributions are expected, such as the high concentration of permits for offices in the downtown area of San Francsico. But some of the distributions are more interesting and tell a story about the urban makeup of the city. Notice that apartments are much more concentrated closer to downtown in neighborhoods like the Tenderloin, Nob Hill, and Hayes Valley, most heavily around the eastern and northern areas surrounding the financial district. Two-family homes (ie duplexes) have a different concentration that includes neighborhoods like Cow Hollow and the Mission. And one-family homes are in neighborhoods like Pacific Heights, Noe Valley, and Twin Peaks.

The maps here show the number of building permits by the type of building.

Here are some alternate screenshots that are at a more granular resolution, so you can see a bit more detail on the different areas of the city. Click each thumbnail for a much larger version:

Try it for yourself

You can open up the sample report that we created to visualize these building permits.  The report will load with two layers: the building permits and the neighborhood boundaries of San Francisco. You can change which types of building permits are shown by selecting items in the “Proposed Use” filter pod that is open in the report.

• 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 on the data

It’s always important to remember what data we’re looking at. This is the number of building permits issued between September 2008 and August 2009. A single building might have multiple permits issued, which could be everything from renovations or re-roofing to a change from residential to commercial, etc.

This is the third part of an ongoing exploration of publicly accessible San Francisco data from DataSF.org. Please see the other posts in the series.

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

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.