When a heart stops beating (asystole), there are only few minutes left for reanimation. The last effort to come back to life is much more successful when using a defibrillator or done by experts. Recently, there were several defibrillators installed in Cologne that can be operated even by an amateur and should increase the survival rate in case of an asystole.
Nevertheless, the success of a reanimation is strongly connected to time. While an instant treatment can save the patient’s life in about 90 per cent of the cases, the survival rate decreases by 7 – 10 per cent per minute. Therefore, it is crucial to be close to either one of these public defibrillators or to be in a close range to an ambulance.
The impressive open data site Offene Daten: Köln provides the geographer with a bunch of useful geodata for whole Cologne. Besides detailed vector data for roads and addresses (a 157.415 point shapefile!!!), you will also find a dataset on the defibrillators. I only had to geocode the sites of ambulances, which are most of the time also the city’s fire stations.
To prepare the analysis, the road data had to be manipulated because the research should also include sites of ambulances that are close but not in Cologne’s municipal area. Thus, I digitized some of the major roads that would be used by the ambulance when having an operation in the city. To digitize the access roads, QGIS and the OpenStreetMap WMS turned out to be comfortable tools. It is important to create a new feature for every junction. Otherwise you will experience problems in the analysis afterwards.
After preparing the route data, I moved to ArcMap because it includes the Network Analyst, which is quite handy for such kind of analysis.
Before starting to create service areas for the defibrillators and the ambulances, you will have to build a road Network Dataset that can then be used in the Network Analyst. By creating the service areas, the Network Analyst will take different locations (e.g. fire stations and defibrillators) and the road network to calculate polygons that show the accessibility based on your defined distances.
According to the literature an asystole cannot be survived more that 8 to 10 minutes in most cases. Therefore, I decided to estimate the distances that you can cover by foot and by ambulance in this period. For the defibrillators, it seemed to be reasonable to take a decreasing running speed of around 18 to 11 km/h and for the ambulances I decided to assume an average speed of 50 km/h. You should not forget that the utilization of the defibrillator usually does not only include the running to the defibrillator but also the rushing back. Thus, the covered distance for the defibrillators must be halved. For the ambulance, you can also include one minute for the emergency call and the departure of the vehicle.
When calculating the service areas considering the coverage per minute, you will have polygons that indicate where help arrives within 1 to 9 minutes.
Right now, there is only one major mistake. Due to the overlapping of the defibrillators service areas, some areas are coloured red although they are much better covered by the ambulance. To fix this issue, you will have to merge the data and either set a hierarchy for the displaying of the areas (high preference for minute 1, low preference for minute 8) or clip and intersect some data.
Initially, I planned another style of visualization, which is why I did not bother that much on how to present the polygons. Due to the availability of a point Shapefile that includes every address in Cologne, I decided to symbolize them according to the appropriate service area. By this kind of presentation, the viewer is enabled to zoom in and identify its own house or even search for it in a web based map.
The addresses can be extended by the attribute value indicating the minutes until arrival of help by performing a spatial join. Using the merged service areas and the point Shapefile, it is possible to only join the minimum value of an attribute to the target layer. This option resolves the problem of overlapping polygons with different values and stores the minimal arrival time for each address independent on whether it is served by a public defibrillator or the ambulance.
For the final styling I switched back to QGIS because I feel much more comfortable and powerful using the QGIS Print Composer. Due to storage limitations and diverse issues, my plans to also publish the data in a web map are not yet implemented but I hope to find a satisfying solution very soon.
You can see, that there are only few locations that are endangered drastically. The city’s center is well covered by ambulances and defibrillators. It would be interesting to play a little bit with the variables for the coverage of the ambulances and defibrillators. Possibly my assumptions are over- or underestimating the reality. Moreover, it is likely that I missed one or more sites of ambulance vehicles.