Tag Archives: GIS

See What Others Can't

Ever since I was a kid, I wanted a superpower of some kind. Little did I know that one day my wish would actually come true. 

For anyone who is a serious user of Geographic Information Systems (GIS), it is not news that this week is the 2019 Esri User Conference. If you are not one of those people, the “UC” is an annual gathering of around 20,000 people who share an interest in applying geospatial technology to solve real-world problems from optimizing business to saving the environment. I was particularly inspired by the theme this year, “See What Others Can’t.”

At its core, GIS is a spatial database for the analysis and visualization of information. When it is used in EMS, it can take a deep dive through your call history and come up with an estimation of the likelihood of the location of calls for service within the next hour. Because it can be an automated process, this forecast can be repeated every few minutes to give you a constantly updated view of the near future regarding where you are most likely to be needed. Some users of MARVLIS Demand Monitor compare it to a weather map that shows the changing conditions in your service area. But knowing where you need to be is only a part of the problem of optimizing the delivery of emergency medical services.

To really be efficient, you also need to know where you are and where you can be within your response time allocation. To answer this question, you need a model of the street network and an understanding of both the daily patterns of travel as well as the unique driving conditions right now. Many counties across the US have dedicated GIS staff to maintain these navigation and addressing models, but commercial vendors can also provide a good base layer of data. TheAddresser is another product from BCS and it can be used to measure or even improve the quality of your geographic data to improve its ability to turn an address into a proper coordinate where a crew can physically respond. The digital road network that is used to calculate a route can be improved by modeling how fast vehicles in your fleet have traveled along each road segment in the past, divided by direction, and lumped into various traffic time periods. The MARVLIS Impedance Monitor automates the mining of your Automated Vehicle Location (AVL) history to generate these unique travel times to understand exactly what area can be covered even as an ambulance is moving. For the immediate hazards along the way, MARVLIS can leverage the events logged by Waze users in real-time to enhance your own road network data through MARVLIS Central. Together, this gives you the best understanding of the reach your crews have at any given moment.

The real trick is in how you choose to post ambulances to meet your specific objectives. If a fast, safe response is most valued, ambulances can be directed to uncovered hot spots which will minimize the distance they must travel to the next call. If cutting response times across the board, or minimizing post moves is preferred, a weighting can be applied in the MARVLIS Deployment Planner to optimize the geographic coverage area. Regardless of how the criteria are balanced, an hourly, prioritized posting plan can be generated based on your service objectives. That plan can then be automated through the live connection in MARVLIS Deployment Monitor that can not only see where ambulances are located by their status, but also directly viewing where calls are currently active from the Computer Aided Dispatch (CAD) software. It can then even make specific recommendations on reassigning units to automatically optimize your coverage criteria.

Together, these intrinsically GIS-based tools can provide an unparalleled insight into the operational world of EMS with timely automated recommendations on how to improve service according to your community’s values. The suite of MARVLIS applications give any EMS manager a view to “see what others can’t.”  To see clarity in the everyday chaos of EMS operations, GIS can give you genuine superpowers. 

-Dale Loberger

 

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Filed under Administration & Leadership, Command & Leadership, Dispatch & Communications, EMS Dispatch, EMS Topics, News, Technology & Communications, Training & Development, Vehicle Operation & Ambulances

GIS for EMS

Both acronyms (GIS and EMS) represent not just technologies, but fields of study and service that have very old roots even though each can trace their modern form to research starting in the 1960s.  Both have witnessed explosive growth and application far beyond their original vision.  But most importantly, these two names definitely belong together.

Those who have any knowledge of Geographic Information Systems (GIS) will often think first of maps at the mention of its name.  Maps, however, are simply the form GIS professionals use to express the actual work done with a GIS.  That work consists of maintaining a descriptive spatial database and using that database to perform analysis that answers real-world questions or solves domain specific problems.  There are many examples of how it can be applied, but here we will discuss just those in support of Emergency Medical Services (EMS).

At the very simplest end of the spectrum is printed mapbook production.  Because GIS “maps” are stored as data rather than graphics, they are easily edited and symbolized in different ways to meet different objectives.  For use in ambulances, maps should be quick references primarily showing roads (with street names and block addresses) and landmarks essential for navigation.  Street index creation is an automated function of the GIS that can make a printed book of maps more useful for crews attempting to find a specific street.  Better still is an interactive map – one that can locate your current position using GPS and can automatically search an address (a process called “geocoding” or “geovalidation“) and recommend an efficient route between these two points.  This function is manual in printed form but interactively can leverage historic “time-aware” travel impedances (the actual time it takes to travel a certain road segment in a specific direction given the current time of day based on your own past experience) and even access known road closures due to ongoing accidents or scheduled construction to provide realistic travel times and routes given current conditions.  The database can also be used to locate not just the closest vehicle, but make unit recommendations based on additional criteria such as special equipment or training.  When these interactive maps are used with ruggized touch-screen computers or new tablet devices, you have a powerful combination that can also support ePCR charting or other applications.

When a fleet of ambulances can provide positional and status information to the call center, the dispatchers have a better situational awareness of the functioning system in real time.  Then by using additional GIS functionality to map previous incidents, a “hotspot” map (a map showing the areas of highest likeliness for generating a call) can be created to forecast future demand using simple predictive analytics.  In the past, some organizations have poorly implemented a form of System Status Management (SSM) that failed to meet the objective of increasing efficiency and left many paramedics soured on the idea of post moves.  Effective implementations (some highlighted in past blogs here) have shown that Jack Stout’s idea can be properly done in almost any system using modern technology.  Moreover, by positioning ambulances closer to their next call, not only is response time reduced but the incentive to be hasty in that response is also reduced leading to less risk in travel.

Beyond these daily tactical applications of GIS, there are many potential strategic ones.  Preventing a call is better than an emergency response at any speed.  By looking beyond just the calls for service in the coming hour, we can begin to look further into the future and recognize specific risks of target lifestyle groups.  Preventive care or community wellness programs can be directed at the most vulnerable populations to maximize the investment of such a program.  Locating groups with increased potential for cardiac problems can aid in locating a blood pressure screening event as one example.  Some agencies have turned to GIS to help them find new recruits or volunteers.  I encourage you to communicate with your local GIS staff and let them know how they can help you.  After all, assisting you to become more efficient helps them show value as well.  You do not need to know the details behind the analytical tools, it is your existing knowledge of the community and its needs that will help your GIS staff address them.  If you lack those resources locally, or have specific questions, please make a comment below and I will follow up with you directly.

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Quick Thoughts from the Esri UC 2011

The Esri User Conference in San Diego each summer is the largest GIS event of the year and there was certainly no disappointment this week as the biggest crowd ever gathered for the first day plenary sessions. According to Jack Dangermond, founder and president, the plenary held over 14,000 people with more than 15,000 expected in the final total by the end of the week.  There were plenty of announcements made by Esri which were enthusiastically received even though many had been “leaked” during the weeks before.  Jack’s own famous (and lengthy) pre-conference Q&A provided hours of early study material as did The Road Ahead for ArcGIS article in the summer issue of ArcNews for those who wanted a preview of what we would hear today.

While it would be a monumental task to cover everything presented, the highlights I think that are appropriate for public safety agencies to consider are more manageable and most significantly are not necessarily technology based.  A major thought Jack drove hard was a discussion of “understanding understanding” or the role that GIS plays in making information understood.  It wasn’t all about new cloud-based services, but extending a practical concept of “one map”.  That is the creation and authoring of data, mashed together into “intelligent web maps” and disseminated for collaboration.  After all, GIS is not just about visualization, but powerful analytics and even the value of business management.  The focus of the morning was clearly functional – from an operational perspective rather than just pandering to the technologists.  While there was plenty of demonstration of specific new tools coming in version 10.1, the driving factor was definitely value and productivity.  Another interesting concept that was clear was the co-evolution of GIS with related technologies like 3D (specifically LiDAR or even “indoors” and visualization rendering), imagery, and social media (“crowdsourcing”) forming a practical platform for analysis, problem-solving, and prediction.

Most surprisingly was that the word “cloud” was not used much at all, however the evidence of the platform was clear in new managed service options coming available through ArcGIS Online which has become a true platform to simplify and help manage the elastic demand for “intelligent web maps” during disasters.  These Esri subscription services will soon be available through ArcGIS Online.  While some critics bristle at security concerns or a perceived lack of control, this option is increasingly interesting to many emergency managers especially as bandwidth-intensive GIS maps take a bigger role in sharing situational information in crisis management without administration hassles.  ArcGIS Explorer is also growing up with new capabilities to read services like KML and WMS as well as produce Microsoft PowerPoint-style presentations with even more interactive geographic story telling capabilities.  Additionally, ArcGIS Online is becoming time-enabled and even more timely in its ability to share layers from many diverse sources represented uniformly.  ArcGIS Online also is getting significant new basemap options such as oceans for marine studies and publication quality National Geographic cartography.

The Community Analyst is another little known secret application from Esri providing flexible tools for searching and summarizing demographic data.  A free 14-day trial of the application is available for evaluation.  Imagery will add many new powerful tools at 10.1 making it faster and more useful with options to measure 3D qualities similar to Pictometry.  And functionality from MapIt is now being repackaged as ArcGIS Server templates to integrate Microsoft SharePoint or IBM COGNOS.  Another popular announcement was native 64-bit support for ArcGIS Server.

Several application examples from the “Special Achievement in GIS” (SAG) award winners were quickly displayed and more lengthy reviews of applications from the City of Boston were also provided as examples of “footprints for us to follow.”  You can watch recorded videos of the plenary sessions  and more online.

If you were there yourself, what was your favorite memory?

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Addressing Geovalidation

The most fundamental aspect of an E-911 emergency dispatch center is to be able to locate a call for service and communicate that location to the closest appropriate vehicle to be dispatched.  In nearly every case, that location description is eventually an address.  The back-end process starts when a call is placed to 9-1-1 from a traditional wired land-line and its Automatic Number Identification (ANI) is compared to phone company records to find the Automatic Location Identification (ALI) address which is then compared to the Master Street Address Guide (MSAG) database to determine which Public Safety Answering Point (PSAP) or “call center” will receive that call.  It is the dispatcher at the PSAP who will determine the required resources and ultimately dispatch the requested assistance.  For cellular phones, VOIP, or telematics, the process is a little more complex to return a current latitude/longitude coordinate rather than a pre-determined address.  In those cases, the PSAP will interpret the caller position to a nearest address using Geographic Information System (GIS) technology.  The process of turning an address into a latitude/longitude value is called “geocoding” by GIS people and “geovalidation” by EMS staff.  The inverse of that process, finding the address of a specified point, is preceded with the term “reverse” by either crowd.  So, regardless of how the location information is presented to the PSAP, the closest resources can be found by comparing points and, in return, an understandable location descriptor can be provided for any point – at least in theory.

The ability of the GIS to translate addresses into coordinates in order to determine the closest responder (or the reverse process of turning an incident location into an address to communicate that location) is based on the quality of the geographic street database and its attributes.   Some US counties create their own data, but many others use street data from national level providers including the US Census Bureau who provides free TIGER/Line files and commercial sources such as TomTom (formerly TeleAtlas) or NAVTEQ as a foundation.

The accuracy of the street centerline shape for any segment of road allows a coordinate to be located precisely along the correct street and the address range attributes allow a position to be accurately described with an address.  So to make this all work, the centerline shape of your data must reflect the actual shape of the road on the ground and be segmented (typically broken at street intersections) to allow address ranges to be defined over limited distances.  These attributes typically include left-from-address, left-to-address, right-from-address, right-to-address (relative to the increasing direction of the numbers), prefix or direction, street name, street type, and suffix or direction for each segment of street at a minimum.  This all sounds very easy, but the real world always gets more complicated than our simple schemes.

According to David Hunt, GIS Analyst and 911 Coordinator for Wake County in North Carolina, “the whole country has been actively converting [rural] route and box numbers to addresses over the last 25 years,” but the process has not always been consistent.  The distance between addresses was not always uniform before the general availability of GIS for addressing.  Good centerlines make the address assignment process much easier for new subdivisions using GIS, but reflecting current addresses in an automated fashion is still not always straightforward.

Consider the following example from an older part of town where the house numbers are sequential based on structures rather than an increment distance as is more common in modern neighborhoods.  The house in the center is 502 and the structure next door to the east is 504 followed by 506 at the end of the street.  The structure on the west side of the middle house is 402 since there used to be an alley (but which is now closed) between those properties.  The next structure further west is on a corner and numbered 400.

So technically this street segment includes the 400 AND 500 blocks of this street.  The “right-from-address” would clearly be 400, but what is the “right-to-address”? Since the next block starts at 600, it could logically be either 506 or 598.  What is the difference?  Well, the greater ending value allows for more possible matches of any addresses searched but also provides approximation of non-existent addresses in a reverse-geocoding process. If a point query returned an address communicated as “410 S Washington St”?  Would it be located just 5% along the length of this street putting it at the actual structure numbered 400 or is there something between the structures at 402 and 502 that we need rescuers to find?   The smaller ending value limits some of the possible addresses and would not allow a possible match for a search on a non-existent house number like 510.  However, unless the segment is broken between 402 and 502, as it once was in reality, it would return an estimated coordinate for a query on a fictitious 404 instead of suggesting an entry error.

Another way around the problem is point-based addressing with GIS as managed in Sumter County, SC which limits queries to only actual house numbers in the geovalidation process.  Instead of applying the range attributes to the graphic representations of the centerlines, points are entered for each structure.  This also simplifies the problem with addressing mobile homes which need to change address as the actual dwelling is relocated.  “Making the digital address points more easily accessible helps create less issues,” says Emily Banar, GIS Analyst for Sumter County in charge of addressing.

Individual geocoding services provided through the GIS, named “locator services” by Esri, can be combined as “composite locator services”.  A composite geocoding search  is one that uses more than one single locator in order to attempt to match all possible addresses against more than one geographic dataset.  Combining search methods can provide more reliable geovalidation for your High Performance EMS by using a point range before a street range.

TheAddresser™ is an application built for both ArcGIS Desktop and ArcGIS Server by BCS as a complete address management system for both street ranging and single point addressing.  Business rules are built-in to the application logic to manage overlapping ranges, out-of-range addresses, as well as others to ensure that street centerline ranging and single point addresses stay in sync with each other and address accuracy is maintained.  This system also includes street name management and reporting of existing address errors.  Other tools of this system aid in the ease of editing street centerlines and address points to make sure that your GIS-based geovalidation process works as it should.

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The Future of Prediction

I have read the positions stating that calls for emergency services are completely random (justifying the reason they are often called “accidents”) and therefore not able to be predicted.  But both academic literature and practical experience show that demand prediction can be an effective tool in helping to balance scarce resources (ambulances and their trained crews) with public demand (requests for emergency responses even without taking into account the abuses to the system as discussed in a previous posting on the problem of “frequent flyers”) while still improving response times and controlling costs.

For anyone who thinks all of this sounds too good to be true, there are examples of where expensive technology is not having the desired affect.  One such location is Lee County EMS in Florida where not only have response times not been improved, but ambulances are burning more fuel than ever and the critics include the very paramedics it is supposed to help.  While predicting where the next 911 call will come from may be similiar to “picking the winning card at a casino” as the Florida investigative news reporter suggests, that isn’t really the objective.  We don’t need to know which phone will make the next call, it is enough just knowing the probability of a call coming from any given location within the service area.  This may be a subtle distinction, but one that makes a huge difference at MedStar in Fort Worth or Life EMS in Grand Rapids where response times were dramatically improved by taking the next step beyond simple demand prediction and placing ambulances at positions where they can be the most effective.

Academic studies show that demand pattern analysis can be used without hourly, daily, or seasonal calibration to achieve potentially acceptable tolerances of demand prediction, but when adjusted with these appropriate corrections, software applications like MARVLIS (the Mobile Area Routing and Vehicle Location Information System) can effectively predict demand in practical situations.  According to Tony Bradshaw of BCS, the makers of MARVLIS, it routinely calculates where about 80% of demand will occur and when paired with realistic drive-time response zones it demonstrates valuable support for a dynamic System Status Management plan to pre-position, or “post” ambulances closer to their next call saving valuable time and increasingly expensive fuel costs.

What matters most, though, is what agencies experience in the field.  At SunStar they say ” the most significant result was improving our emergency response time from 90.2% to now over 93% in lieu of an increase in patient call volumes.  This equates to ambulances arriving on scene more than 1 minute quicker.  We additionally saw a savings of $400,000 in penalties by exceeding our contractual goal of 92% and performing above 93% compliance.”  Similarly, Steven Cotter, Director of Sedgewick EMS added that “the technology has opened our eyes to be able to understand how we are performing, where we are deficient in our performance and how we can make changes quickly and adapt to a changing environment.”  And beyond simple response times, “it’s what technology should do,” says Joe Penner, Executive Director at the Mecklenburg EMS Agency, ” take the complex and present useful, straightforward information.  It has helped us improve response times, resource utilization AND simultaneously reduce unnecessary post moves — your patients and employees will appreciate it!”

My conclusion is that proper demand prediction paired with realistic response creates significant opportunity to improve performance and cut costs even in growing communities.  When used properly, the future looks bright for High Performance EMS!

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