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 (12)
Yes, we occasionally offer informational sessions such as lunch talks or webinars. During these sessions, we provide insights into the tool, demonstrate specific use cases, and answer questions about how to use it.
We share updates on upcoming events and other information in our newsletter.
mobi.mapr does not use any tracking methods to identify individual users. No personal data is collected or stored.
For the technical operation and further development of the platform, only anonymized usage data is collected—such as which features are used and how often. This data is used exclusively to improve the application and ensure stable operation.
If you have any further questions, please feel free to send us an email: mobimapr@bw-im.de
If you have any further questions or would like to provide feedback, you can contact us by email at any time:
mobimapr@bw-im.de
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.
mobi.mapr aims to make mobility data understandable, accessible, and comparable—for everyone who wants to understand and shape mobility: from policymakers and government agencies to citizens, initiatives, and businesses.
To this end, the platform simplifies complex relationships and presents them in an easy-to-read format on maps. This reveals how mobility actually works in everyday life.
mobi.mapr is open to everyone—no registration required and free of charge. This creates a shared, fact-based foundation for discussion, analysis, and local decision-making.
mobi.mapr is based on a combination of various data sources that map mobility from different perspectives.
To map everyday destinations, mobi.mapr primarily uses open geodata—specifically from OpenStreetMap. Depending on the research question, additional datasets are also incorporated.
For population distribution, mobi.mapr relies on official statistics, such as those from DESTATIS.
Furthermore, insights into mobility behavior and qualitative aspects are incorporated into the analyses—such as the perceived quality of routes, for example from the ADFC Bicycle Climate Test.
All data is consolidated into a unified model and systematically evaluated. An overview of all data sources used is available directly in the tool via the info icon.
Everyday mobility refers to trips made regularly in daily life—such as for shopping, work, school, leisure, or socializing.
The focus here is not on individual trips, but on how easily people can reach their typical daily destinations.
The selection and weighting of these activities are based on the study “Mobility in Germany,” which maps the actual mobility behavior of the population. This ensures that the analysis reflects real-world daily needs.
mobi.mapr shows how well mobility works in everyday life—that is, how easily people can reach everyday destinations such as shopping, work, or leisure activities.
At its core, the analysis is based on the accessibility of these activities: the best routes are calculated for every location and every mode of transportation combination. In addition to pure travel times, realistic factors are also taken into account, such as additional time requirements (e.g., for parking) or qualitative aspects like satisfaction with mobility conditions (e.g., from the ADFC Bicycle Climate Test).
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 is a project of the Baden-Württemberg Institute for Sustainable Mobility (BWIM)—an independent think tank and action group dedicated to sustainable mobility.
The platform is backed by an interdisciplinary team of experts from various fields who are continuously developing mobi.mapr.
For more information about BWIM, visit bw-im.de.
Data (5)
mobi.mapr uses different data sets.
External data—such as population distribution data—is updated as soon as it is republished by official sources.
Internal data, such as the underlying activities and calculations, is updated on an ongoing basis. This is because calculating mobility analyses is a very complex process that can sometimes take several weeks or months.
To ensure that the results provided are as up-to-date as possible, the dataset is continuously refined and updated.
An overview of all data sources used is available directly in the tool via the info icon.
mobi.mapr is based on a combination of various data sources that map mobility from different perspectives.
To map everyday destinations, mobi.mapr primarily uses open geodata—specifically from OpenStreetMap. Depending on the research question, additional datasets are also incorporated.
For population distribution, mobi.mapr relies on official statistics, such as those from DESTATIS.
Furthermore, insights into mobility behavior and qualitative aspects are incorporated into the analyses—such as the perceived quality of routes, for example from the ADFC Bicycle Climate Test.
All data is consolidated into a unified model and systematically evaluated. An overview of all data sources used is available directly in the tool via the info icon.
mobi.mapr evaluates mobility not from the perspective of service offerings, but from the perspective of people’s daily lives (activity-based rather than service-centered): While traditional analyses often focus on infrastructure, network coverage, or service frequencies, mobi.mapr shows how effectively people actually reach their daily destinations—such as shopping, work, or leisure activities.
To do this, mobi.mapr combines objective route data with real-world factors (e.g., time spent parking) and additional qualitative aspects, such as infrastructure features or survey results, and synthesizes them into a unified assessment.
The analysis is conducted at a granular level based on a Germany-wide hexagonal grid. This makes differences visible—even across administrative and settlement structure boundaries.
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 uses open geodata—particularly from OpenStreetMap—for many everyday destinations. We rely on the information available there.
It may happen that certain everyday destinations are missing, no longer up to date, or have been relocated. In such cases, these discrepancies are also reflected in the analyses.
You can help improve the database: Missing or outdated information can be added directly in OpenStreetMap. This information will be incorporated into mobi.mapr during one of the next regular data updates.
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 (15)
Yes, that’s possible. First, select a location by clicking on it on the map. Then, right-click to select a second location and add it for comparison.
Yes, you can use the icons in the left column to include or exclude specific modes of transportation and view different combinations.
At least one mode of transportation must always be selected. Walking is always selected by default and cannot be deselected, as this form of travel is generally possible everywhere and serves as the basis for the calculation.
Please note: When using public transportation, routes are usually combined with walking—for example, to access stops or when transferring.
We currently do not offer individual results reports. If you’d like to delve deeper into the analyses, you can access the underlying data directly. This data is available via the download button at the very bottom of the tool (e.g., as GeoJSON) as well as through the API.
This allows you to create your own analyses or process the data further in GIS software such as QGIS.
If you have specific questions, we’re happy to assist you. Just contact us at mobimapr@bw-im.de
Yes, you can assess accessibility based on specific activities. Clicking on the map will show you how accessible various everyday activities are in a given area. When you select an activity, the individual criteria used in the assessment will be displayed.
The results provide initial insights into possible reasons for the assessment, such as a lack of infrastructure or poor transportation connections.
You can zoom in deeply on the map. Please note, however, that the smallest unit of analysis is the hexagon. These are roughly the size of a city block and therefore do not reflect the conditions of individual streets.
You can enter cities or regions directly using the search bar (“Search for location”) in the top-left corner of the tool. The tool will then automatically zoom in on your selection.
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.
The colors on the map display different information depending on the view:
In the Mobility Quality view, the results are displayed on a rainbow color scale—similar to the Nutri-Score, with ratings ranging from A (very good) to F (poor).
In the Travel Time view, the required travel time is depicted on a monochromatic scale. The colors on the map indicate travel time, ranging from dark blue (short travel times, 0–15 minutes) to light blue (long travel times, over 60 minutes). The darker the color, the shorter the travel time.
The color scales help users quickly understand and interpret the results.
The “Top Performers” ranking shows the regions with the best results—depending on the selected criteria, such as time or quality.
Which regions appear at the top of the ranking therefore depends on the selected criteria and the currently selected level. You can adjust these settings in the tool at any time.
When you click on an area or a hexagon in the tool, detailed information such as ranking, rating, population data, and the distribution of activities appears in the bottom right corner. Each column is clickable: You can first view a more detailed overview of the activities included and then see the specific locations on the map.
This allows you to see, step by step, what went into the rating.
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.
In mobi.mapr, you can analyze mobility by different modes of transportation. You can choose from “walking,” “bicycle,” “public transit,” and “car.” You can select various combinations of these modes to see how local mobility options change.
At least one mode of transportation must always be selected for an analysis to be performed.
“Walking” is always selected and cannot be deactivated, as this form of transportation is generally possible everywhere and serves as the basis for the calculation.