Not logged in
PANGAEA.
Data Publisher for Earth & Environmental Science

Koster, Philipp (2018): Areas-of-Interest from OpenStreetMap (Switzerland). PANGAEA, https://doi.org/10.1594/PANGAEA.892644

Always quote above citation when using data! You can download the citation in several formats below.

RIS CitationBibTeX CitationShow MapGoogle Earth

Abstract:
This dataset contains Areas-of-Interest (AOI) derived solely from OpenStreetMap data as geospatial data (polygons) covering Switzerland.
The objective of Areas-of-Interest (AOI) is to convey visual information to a user (map reader or tourist) where there are areas are of "high interest" regarding facilities like shopping, eating, accomodation, sightseeing or leisure.
The use of this file comprises touristic applications as well as urban planning and location-allocation analysis and site selection. The preferred spatial resolution of this dataset is between 10 and 2 meters on the ground (zoom levels from 16 to 14).
A main input of AOI are Points-of-Interest (POI). Typical POI are restaurants, bars, shops or museums. While POI are mostly punctual, the geometry of AOI is of type area or polygon. In fact, AOI can be also based on the street network and potentially on more information like human location tracks. Google introduced 2016 AOI in their map products and visualized it as orange shades. Google is reportedly using human location tracks to derive information of "high activity" but did not disclose the algorithms behind their AOI layer.
The goal of the research behind this dataset at hand was to produce AOI with a reproducible process which is based on open data, specifically POI and pedestrian routing data from the OpenStreetMap crowdsourcing project. The AOI are defined here as "Urban area at city or neighbourhood level with a high concentration of Points-of-Interests (POI) and typically located along a street of high spatial importance". Roughly five processing steps are used to generate these AOI: (1) filtering relevant POI (taking POI from OpenStreetMap as input), (2) spatially clustering selected POI using the DBSCAN algorithm, (3) creating areas using concave hull algorithm, (4) extend the resulting areas with a certain spatial buffer based on a network centrality algorithm (taking routes as input), (5) sanitizing the AOI e.g. by removing water areas and eliminating sliver polygons.
Related to:
Koster, Philipp (2018): Big Spatial Data Analysis and Processing. Master thesis, HSR University of Applied Sciences Rapperswil, Switzerland (unpublished)
Coverage:
Latitude: 46.800000 * Longitude: 8.230000
Event(s):
Switzerland * Latitude: 46.800000 * Longitude: 8.230000 * Location: Switzerland
Size:
1996.0 kBytes

Download Data

Download dataset