Spatial analytics is a technology used by governments everywhere to understand where things happen, when, and why. It enables data to be seen and shared among officials as well as with the public itself, and has been used to map critical information for decades. Geographic information systems (GIS) that utilize this technology allow better and more accurate decisions to be made. But only recently has the data that governments have at their disposal increased exponentially, as well as the ability to see these massive quantities of data and make sense of them in real-time.
That data, known as big data, comes from a variety of sources — sensors, demographics, drone imagery, and even social media. These vast amounts of information that the human brain can’t conceivably order, let alone make valuable use of when translated into the visual form of a map, can become powerful collaborative decision-making tools for local governments.
One way that states are using the power of location intelligence to improve their communities is in producing more accurate counts of homeless populations. Often, the key problem in addressing homelessness starts at the fact that stakeholders have no idea how many homeless there are, nor where they spend their time.
Every year in late January, communities across the United States conduct a count of their local homeless populations. These one-night snapshots are reported to the U.S. Department of Housing and Urban Development (HUD) to provide a better understanding of the scope of homelessness and to measure progress in the effort to reduce it.
In Colorado, the City of Aurora found a way to get a more accurate count for this year’s point-in-time homeless census. Using a location-enabled survey app deployed on mobile devices, the city — which has a population of about 360,000 — identified over 100 more homeless people than the 2016 count recorded, and easily documented each person’s location. Once the survey was finished, the analysis helped identify pockets of homelessness that had previously gone undetected.
Emergency responders are also using spatial analytics to save lives by taking advantage of many disparate forms of data.
For instance, firefighters responding to wildfires must have access to real-time data to be where they need to at the right time. But they can also now integrate other forms of information into that decision-making process that would have been difficult to use in the past. Several key metrics are all able to be seen on a map and factored in when responders coordinate to put out a fire — historic fire trends in an area, the path that flames take, and vegetation patterns, such as where dry or recently cleared brush might exist now all figure into operations.
Access to this data, and the ability to use it and share it amounts to effectively making predictive analyses when it comes to fire response and prevention, rather than reacting to fires when it might be too late. Now, crews will know to arrive at the site of a small brushfire early with more equipment than usual because it is historically an area that burns fast, it is the late summer, and they can see that current traffic conditions will compromise access.
Crowdsourcing is another area that is sometimes overlooked as an important part of data analytics.
The ubiquity of mobile devices and social media make citizens more connected than ever before, allowing responders to use citizen input to assess emergencies like natural disasters and mitigate the resulting damage faster and more accurately. Leveraging “citizens as sensors,” local government officials have the tools and data to respond and fix the issue in a more targeted informed way.
One area where there has been significant improvement keeping people safe during events, like the tornadoes that devastated the Southeastern United States earlier this year, is real-time awareness of the state of a natural disaster area. Both dispatchers and field personnel must understand how large-scale events have unfolded, and where the greatest need for action is to save lives and reduce damage.
In particular, the information that can be gleaned through citizen smartphone use is a form of social data that helps to provide context. Not only does information sourced from the public serve as a valuable reservoir of situational data, but it is generated in real time and from first-hand observations.
This crowdsourced information can be integrated with incident response systems, adding layers to agencies’ authoritative data to enhance situational awareness during and after a disaster. This combination can provide additional valuable new forms of information from sources on the ground in and around the emergency.
Customized apps can be created for emergency response agencies to use in order to gain better understanding and awareness of disasters — and their subsequent impacts. By combining crowdsourced information from mobile devices and social media data with spatial analytics, officials may be able to prepare for and respond to a disaster faster than ever before.
Collected sensor data, such as that from river-height gauges and seismic monitors, when combined with this crowdsourced information, provides a holistic real-time picture that enhances situational awareness, and may even assist in predicting unforeseen events resulting from the original disaster.
Ultimately, the massive amounts of data available from the different sources in a community are useless unless people have the right tools to place that data into context and understand it. Location intelligence gives this data a visual representation that isn’t just a picture — it’s a living virtual system as complex and full of continuously updating information as the real world. It’s a tool for creating a more collaborative world, but also one where challenges can be met on a map long before they happen.