ohsome
© OpenStreetMap and contributors
ohsomeHeX
ohsomeHeX is a web-based application to explore the history of OpenStreetMap
© OpenStreetMap and contributors
ohsome Dashboard
Generate accurate statistics about the historical development of OSM data for an arbitrary region
© OpenStreetMap and contributors
OSM contributor activity as earthquake response
3D space-time visualization of building edits after Nepal earthquake in 2015
OpenStreetMap History Data Analytics Platform
The Big Spatial Data Analytics team at HeiGIT is currently developing the ohsome platform. Our aim is to make OpenStreetMap’s full-history data more easily accessible for various kinds of data analytics tasks on a global scale. Applications of the ohsome platform range from web dashboards over data quality assessment to custom data analysis.
Performance
The OpenStreetMap history dataset is large and, thus, requires efficiency in terms of storage size, data access and processing performance. These benefit from a custom-built, compact data representation and from parallelizing computations.
Simple and generic APIs
Two generic interfaces (APIs) are available to enable arbitrary analyses for different user groups. The complex internal data representation is thus encapsulated in order to make it more user-friendly and easy to maintain.
Lossless information
Analyzing all aspects of data evolution and editing activities requires the data schema to provide lossless information on historical OpenStreetMap
data by maintaining all properties of the original data including its errors.
Analysing the Evolution of OpenStreetMap
OpenStreetMap provides almost the entire editing history of its global data evolution. This dataset is large and, thus, generally hard to handle. The central idea of the ohsome platform is to make OpenStreetMap’s huge information treasure available for a larger public and to develop further analysis functionalities. We achieve this by employing big data technology that we tailored to the specific needs of OpenStreetMap’s history data.
OpenStreetMap History Database (OSHDB)
The OSHDB is the main data backend of the ohsome platform. It has been designed for efficient storage of and access to OpenStreetMap history data. In order to ensure the scalability of the system, we build on a partitioning schema which allows distributed data storage and parallel execution of computations.
OSHDB API
The OSHDB API provides an interface to the OSHDB in the Java programming language. We provide the OSHDB on our GitHub repository for everyone to use, and we appreciate the resourceful feedback we receive from the community.
ohsome API
The ohsome API lets you analyze the rich data source of the OpenStreetMap history. Our REST-based API aims to leverage the tools of the OSHDB through allowing to access some of its functionalities via HTTP requests. You can either perform analyses using a dashboard-like application, or use the ohsome API directly and work with its CSV, JSON or GeoJSON responses.
Data Aggregation
Get aggregated statistics on the evolution of OpenStreetMap elements and specify your own temporal, spatial and/or thematic filters. The data aggregation endpoint allows you to access functions, e.g., to calculate the area of buildings or the length of streets at any given timestamp.
User Stats
Interested in how many users have contributed to OpenStreetMap? Get the number of contributors at any custom time period for your area of interest. The users endpoint also allows you to apply different filters, e.g., to find out how many users have edited street names.
Data Extraction
Retrieve the geometry of the historical OpenStreetMap data, e.g., to visualize the evolution of certain OpenStreetMap elements over time. With our ohsome data extraction endpoint you can get the geometries for specific points in time or all changes within a timespan (full-history).
ohsome Dashboard
Generating accurate statistics about the historical development of OpenStreetMap data for an arbitrary region is now as easy as pie. The ohsome dashboard allows custom filtering of all available OpenStreetMap tags and types and includes administrative boundaries, making the selection of search areas easier.
OSM History Explorer
The OpenStreetMap History Explorer (ohsomeHeX) allows the spatio-temporal exploration of the OSM data on a global scale. Therefore we use the ohsome API to aggregate the data of selected features into a set of world spanning hexagonal grids in a monthly resolution. It resembles the idea of the OSMatrix, but resolves its technological shortcomings and transforms it using current big data technologies for parallel processing in the cloud. To generate the hexagonal grids we use DGGRID version 6.4. Thanks to Kevin Sahr and contributors.
ohsome quality API (OQAPI)
With ohsome quality API (OQAPI) we are implementing a software to compute quality estimations on OpenStreetMap (OSM) data. Any end user interested in OSM quality can use the OQAPI to get hints on the quality of OSM data for their specific region and use case. Several indicators (quality measures) are combined in a report to describe the quality for one specific use case. For the backend of OQAPI we use our ohsome framework.
Further ohsome Projects
| – create the data for time-series maps of OpenStreetMap’s historic development | |
| – historical OpenStreetMap objects as machine learning training samples | |
| – Python client for the ohsome API | |
| – QGIS plugin for the ohsome API | |
| – R client for the ohsome API | |
| – global disaster risk reduction dataset | |
| – statistics and graphs concerning mapping in OpenStreetMap for humanitarian purposes |
Cooperation Projects with HeiGIT Support
| – importance of cognitive coherence in collective decision making | |
| – Information Discovery from Big Earth Observation Data Archives by Learning from Volunteered Geographic Information | |
| – explore the OpenStreetMap database specifically in terms of landuse and landcover information | |
| – shaping climate action in a sound way – case Study Baden-Württemberg/California |
Publications
This link will take you to an overview listing all our academic articles: Publications Big Spatial Data Analytics.