OpenStreetMap POI Analyser: Analytical tool for inspecting location plausibility of new POIs in OpenStreetMap
#The Goal of OPA This repository contains information about statistical and comparison charts, datasets and php+python scripts which are developed over the course of Phd research project by Alireza Kashian @ University of Melbourne. The project is aimed at study of different methods to identify possibly wrong placed POIs in OSM projects in automatic manner. Different topics are investigated to understand the challenges involved with OSM datasets, including the way POIs are tagged globally. This study uses a co-existence association rule mining technique which is more or less known as join based feature-centric colocation pattern mining. In this study, different class of POIs are examined in different spatial regions (a.k.a cities). Co-existence patterns are extracted and stored in databases. An innovative method is used for scoring the probability of existence of a new POI at any position, acting as a kind of decision support system. The system is live at openstreetmap.me.
#Why OpenStreetMap
Keywords: CoLocation Patterns, Coexistence, Spatial Association Rule Learning, Spatial Data Mining, VGI, User Generated Content
#Datasets
Cite as Alireza Kashian
Maintainer Alireza Kashian(University of Melbourne)
License This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.