Here is where you find answers to frequently asked questions about mobi.mapr. If your question is not answered here, we’re happy to answer you directly via email at mobimapr@bw-im.de.
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General (4)
mobi.mapr evaluates mobility based on the accessibility of everyday activities—starting from each individual location. The analysis is based on small-scale areas (hexagons) that cover the entire territory of Germany.
For each of these hexagons, the system calculates how long it takes people to reach relevant destinations—such as shopping, work, or leisure activities—using various modes of transportation. In addition to pure travel times, the calculation incorporates realistic factors, such as extra time for parking or differences in the quality of routes.
Instead of considering just a single destination, the three closest options are included and weighted. This results in an average travel time for each hexagon.
This is then compared with similar regions. The result is an index that describes local mobility quality and shows how well mobility functions—and how it ranks in comparison.
Die Gesamtbewertung basiert darauf, wie gut alltägliche Aktivitäten erreichbar sind – etwa Einkauf, Arbeit oder Freizeit. Dafür wird berechnet, wie viel Zeit Menschen im Alltag benötigen, um diese Ziele zu erreichen – unter Berücksichtigung von Distanz, verfügbaren Verkehrsmitteln und realistischen Wegezeiten (Mobilitätszeit).
Diese Zeiten werden anschließend mit strukturell vergleichbaren Regionen ins Verhältnis gesetzt. So entsteht ein Index, der zeigt, wie gut Mobilität vor Ort funktioniert.
Die Einordnung erfolgt in einem leicht verständlichen Bewertungssystem von A (sehr gut) bis F (sehr schlecht).
Dabei werden die einzelnen Aktivitäten unterschiedlich gewichtet – je nach ihrer Bedeutung im Alltag. So spielt es beispielsweise eine größere Rolle, eine Auswahl an Restaurants zu erreichen, als Briefkästen.
Yes, mobi.mapr is freely accessible and can be used at no cost.
We want to make mobility data understandable and usable for everyone. That is why analyses and results can be used in various public contexts—such as in presentations, publications, academic settings, media reports, or civil society work.
The underlying data is available under the CC-BY-4.0 license. It may be used freely, including for commercial purposes.
The following applies equally to the use of analyses, results, and data:
Please cite the following source:
Or alternatively, in short form:
We would appreciate a brief notification regarding publications sent to mobimapr@bw-im.de.
Mobility time describes the actual time people need to reach their daily destinations. In addition to travel time alone, it also takes into account additional time spent on activities such as parking or locking up a bicycle. Furthermore, for each combination of location, activity, mode of transportation, and profile, multiple possible destinations are included and weighted against one another. This results in a realistic, everyday-life-based time estimate—not for a single route, but for the typical journey to an activity.
Mobility quality classifies this time: It uses an easy-to-understand rating system to show how well the travel time compares to structurally similar regions.
The rating is calculated exponentially—meaning that differences in short travel times carry more weight than those in longer journeys.
Data (1)
mobi.mapr uses the latest round of data from the Mobility in Germany 2023 study.
Mobility in Germany (MiD) is a nationwide, representative survey of households regarding their daily travel behavior, commissioned by the Federal Ministry of Transport.
The data forms the basis for determining which daily activities are included in mobi.mapr and how they are weighted.
Model (14)
mobi.mapr does not assign a fixed weight to different modes of transportation. Instead, mobi.mapr shows you the options available based on the specific combination of modes you select.
When you select multiple modes of transportation, the best accessibility for each activity is taken into account. For example, a nearby supermarket is often best reached on foot, while a more distant one is better reached by bike. Multiple options are also considered within a single activity.
The result therefore does not show you a single “best” mode of transportation, but rather how well you can reach your daily destinations overall—under the selected conditions.
mobi.mapr evaluates mobility based on the accessibility of everyday activities—starting from each individual location. The analysis is based on small-scale areas (hexagons) that cover the entire territory of Germany.
For each of these hexagons, the system calculates how long it takes people to reach relevant destinations—such as shopping, work, or leisure activities—using various modes of transportation. In addition to pure travel times, the calculation incorporates realistic factors, such as extra time for parking or differences in the quality of routes.
Instead of considering just a single destination, the three closest options are included and weighted. This results in an average travel time for each hexagon.
This is then compared with similar regions. The result is an index that describes local mobility quality and shows how well mobility functions—and how it ranks in comparison.
The overall ranking is determined by comparing all regions under consideration. Each region is classified based on its mobility quality and ranked accordingly. >>More information on the calculation
When selecting a specific municipality, the results also show how it compares to structurally similar regions. This comparison is based on the RegioStar7 classification.
This allows, for example, cities to be evaluated not only overall but also specifically in comparison with other cities.
Die Gesamtbewertung basiert darauf, wie gut alltägliche Aktivitäten erreichbar sind – etwa Einkauf, Arbeit oder Freizeit. Dafür wird berechnet, wie viel Zeit Menschen im Alltag benötigen, um diese Ziele zu erreichen – unter Berücksichtigung von Distanz, verfügbaren Verkehrsmitteln und realistischen Wegezeiten (Mobilitätszeit).
Diese Zeiten werden anschließend mit strukturell vergleichbaren Regionen ins Verhältnis gesetzt. So entsteht ein Index, der zeigt, wie gut Mobilität vor Ort funktioniert.
Die Einordnung erfolgt in einem leicht verständlichen Bewertungssystem von A (sehr gut) bis F (sehr schlecht).
Dabei werden die einzelnen Aktivitäten unterschiedlich gewichtet – je nach ihrer Bedeutung im Alltag. So spielt es beispielsweise eine größere Rolle, eine Auswahl an Restaurants zu erreichen, als Briefkästen.
This feature is currently not available in the public view. The reason is that rendering all hexagons across larger regions is very computationally intensive—both for the server and for the browser.
We plan to introduce a subscription service in the future. This will make it possible to display larger regions at the hexagon level as well.
Until then, you can already download larger regions as GeoJSON and further analyze them at the hexagon level in GIS software such as QGIS.
Yes, mobi.mapr is freely accessible and can be used at no cost.
We want to make mobility data understandable and usable for everyone. That is why analyses and results can be used in various public contexts—such as in presentations, publications, academic settings, media reports, or civil society work.
The underlying data is available under the CC-BY-4.0 license. It may be used freely, including for commercial purposes.
The following applies equally to the use of analyses, results, and data:
Please cite the following source:
Or alternatively, in short form:
We would appreciate a brief notification regarding publications sent to mobimapr@bw-im.de.
mobi.mapr takes into account everyday destinations based on real-world mobility patterns. It is based on the study “Mobility in Germany,” which shows the actual routes people take in their daily lives—for example, for shopping, work, education, or leisure.
These activities are translated into specific destinations, such as supermarkets, bakeries, or recreational facilities. They are weighted differently depending on how frequently they occur and how relevant they are to daily life.
Thus, the model primarily incorporates destinations that play a central role in organizing daily life, while less frequent activities—such as day trips—are weighted correspondingly lower.
The underlying categories and weightings are transparently displayed in the tool and are derived from scientific literature.
To calculate public transportation routes, mobi.mapr uses the MOTIS routing engine. It calculates a connection on a weekday (e.g., Tuesday) for typical times of activity (e.g., a visit to a restaurant at 7:00 p.m.).
The analyses are currently based on scheduled timetables (target timetables). Actual factors such as delays, service cancellations, or passenger volume are not yet taken into account.
The results thus show how well public transportation functions under scheduled conditions. Expansions to include additional quality aspects are currently being planned.
Mobility time describes the actual time people need to reach their daily destinations. In addition to travel time alone, it also takes into account additional time spent on activities such as parking or locking up a bicycle. Furthermore, for each combination of location, activity, mode of transportation, and profile, multiple possible destinations are included and weighted against one another. This results in a realistic, everyday-life-based time estimate—not for a single route, but for the typical journey to an activity.
Mobility quality classifies this time: It uses an easy-to-understand rating system to show how well the travel time compares to structurally similar regions.
The rating is calculated exponentially—meaning that differences in short travel times carry more weight than those in longer journeys.
mobi.mapr uses the latest round of data from the Mobility in Germany 2023 study.
Mobility in Germany (MiD) is a nationwide, representative survey of households regarding their daily travel behavior, commissioned by the Federal Ministry of Transport.
The data forms the basis for determining which daily activities are included in mobi.mapr and how they are weighted.
mobi.mapr currently takes into account the following modes of transportation: “walking,” “bicycle,” “public transportation,” and “car.” The analyses are based on average usage patterns.
We are working on refining these profiles in the future—for example, to account for different mobility needs (such as mobility limitations) or additional modes of transportation like e-bikes.
mobi.mapr displays only areas relevant to the analysis of everyday mobility—that is, places where people live or where typical destinations are located.
Areas with no discernible use, such as uninhabited areas, forests, or agricultural land, are therefore grayed out.
To define these areas, mobi.mapr uses population data, which is initially available at a resolution of 1 km² and is then applied to the smaller hexagons. Additionally, data from OpenStreetMap is used to determine where buildings are located. Based on this, the population is distributed within the hexagons.
The values displayed should not be interpreted as exact times for a single trip, but rather as a comparable basis for assessing local mobility quality. They may therefore differ from individually measured times.
This is because mobi.mapr does not show pure travel time, but rather a systematically calculated mobility time. This takes into account various aspects of everyday mobility and thus goes beyond a traditional route calculation.
For example: According to navigation, the trip to the supermarket might take three minutes by car, six minutes by bike, or nine minutes on foot. In everyday life, however, additional time is required—such as for parking or locking up the bike—which mobi.mapr systematically factors in.
At the same time, typical behavioral patterns are also taken into account: For very short distances, people often choose to walk, even if the bike would theoretically be faster—for example, because they first have to fetch it from the basement and then put it away again, which takes additional time.
Furthermore, the algorithm considers not just individual destinations but multiple feasible options, which are weighted against one another—after all, people don’t always want to go to the same supermarket.
Mobility time is therefore not an exact route specification, but a realistic, comparable time estimate for everyday mobility.
mobi.mapr uses hexagons to map mobility at a small scale, independent of administrative boundaries. After all, mobility does not stop at municipal or county borders.
The hexagons have a side length of approximately 200 meters, enabling a realistic assessment of local mobility. This reveals differences within cities or regions that often remain hidden when viewed at larger scales.
Administrative boundaries are nevertheless relevant—especially for political and planning decisions. To this end, the results from the individual hexagons are aggregated into larger units such as municipalities, counties, or states and weighted by population.
Both views are available in the tool and switch automatically with the zoom level.
Tool (5)
The overall ranking is determined by comparing all regions under consideration. Each region is classified based on its mobility quality and ranked accordingly. >>More information on the calculation
When selecting a specific municipality, the results also show how it compares to structurally similar regions. This comparison is based on the RegioStar7 classification.
This allows, for example, cities to be evaluated not only overall but also specifically in comparison with other cities.
This feature is currently not available in the public view. The reason is that rendering all hexagons across larger regions is very computationally intensive—both for the server and for the browser.
We plan to introduce a subscription service in the future. This will make it possible to display larger regions at the hexagon level as well.
Until then, you can already download larger regions as GeoJSON and further analyze them at the hexagon level in GIS software such as QGIS.
To calculate public transportation routes, mobi.mapr uses the MOTIS routing engine. It calculates a connection on a weekday (e.g., Tuesday) for typical times of activity (e.g., a visit to a restaurant at 7:00 p.m.).
The analyses are currently based on scheduled timetables (target timetables). Actual factors such as delays, service cancellations, or passenger volume are not yet taken into account.
The results thus show how well public transportation functions under scheduled conditions. Expansions to include additional quality aspects are currently being planned.
mobi.mapr currently takes into account the following modes of transportation: “walking,” “bicycle,” “public transportation,” and “car.” The analyses are based on average usage patterns.
We are working on refining these profiles in the future—for example, to account for different mobility needs (such as mobility limitations) or additional modes of transportation like e-bikes.
mobi.mapr displays only areas relevant to the analysis of everyday mobility—that is, places where people live or where typical destinations are located.
Areas with no discernible use, such as uninhabited areas, forests, or agricultural land, are therefore grayed out.
To define these areas, mobi.mapr uses population data, which is initially available at a resolution of 1 km² and is then applied to the smaller hexagons. Additionally, data from OpenStreetMap is used to determine where buildings are located. Based on this, the population is distributed within the hexagons.