Regional variations of context‐based association rules in OpenStreetMap

Abstract

As a user-generated map of the whole world, OpenStreetMap (OSM) provides valuable information about the natural and built environment. However, the spatial heterogeneity of the data due to cultural differences and the spatially varying mapping process makes the extraction of reliable information difficult. This study investigates the variability of association rules extracted from OSM across different geographic regions and depending on different context variables, such as the number of OSM mappers. The focus of this study is the spatial co-occurrence of OSM tags mapped inside of parks within eight different cities. Without considering any context variable, most association rules were very region-specific without any rule being valid across all cities. Limiting the association rule analysis to parks based on specific context variables increased the number of rules which are applicable across multiple cities. Furthermore, additional region-specific association rules emerged. The most important context variables were found to be the number of features mapped inside the park, the number of tags and the park size. These results suggest that the mapping process has a significant influence on the emergence of association rules within user-generated data.Therefore, this subject needs further investigation to enable effective usage of OSM data across different cultural realms.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

“…However, it is very important to stress that the quality of final public green space map is highly dependent on the quality of the underlying OSM data. Within our study area, the completeness of the OSM data in regard to buildings, public green spaces and the contextual indicators for public accessibility was very high which allowed for reliable high quality results [75], but this should not be assumed for other geographic areas without prior data quality analyses of the local OSM data. An analysis of completeness of relevant OSM features based on intrinsic data quality indicators [82] could be the first step before undertaking the next steps.…”

“…Therefore, it is very important to identify suitable context indicators which are both culturally relevant in regard to local public green spaces and are mapped with a sufficient level of completeness within OSM when transferring the model to other areas. Data-driven feature selection techniques such as association rule mining can be used to assist in finding suitable indicators [75].…”

“…For the region of Dresden, paths as well as benches and playgrounds are very often mapped within public green spaces in OSM making them good contextual indicators for identifying unmapped public green spaces in OSM. This was explored in a previous study by performing association rule mining on parks mapped in OSM within different cities in the world [75]. Based on these results, the features listed in Table 3 were considered for distinguishing public from private green spaces.…”

“…Land use class and access=* tag were derived in Section 4.3.1. The others were selected based on Ludwig et al[75].…”

Impact of Port Construction on the Spatial Pattern of Land Use in Coastal Zones Based on CLDI and LUT Models: A Case Study of Qingdao and Yantai

“…Currently, the close combination of data mining technology and geoscience studies has solved many scientific problems in geography [12,15]. Among them, the association rule algorithm has been widely used [16][17][18][19]. Association rule mining refers to discovering meaningful associations in a large data set to identify the correlations and laws between elements.…”