Geographic Feature Engineering with Points-of-Interest from OpenStreetMap

Adelson de Araujo, João Marcos do Valle, Nélio Cacho

2020

Abstract

Although geographic patterns have been considered in statistical modelling for many years, new volunteered geographical information is opening opportunities for estimating variables of the city using the urban characteristics of places. Studies have shown the effectiveness of using Points-of-Interest (PoI) data in various predictive applications domains involving geographic data science, e.g. crime hot spots, air quality and land usage analysis. However, it is hard to find the data sources mentioned in these studies and which are the best practices of extracting useful covariates from them. In this study, we propose the Geohunter, a reproducible geographic feature engineering procedure that relies on OpenStreetMap, with a software interface to commonly used tools for geographic data analysis. We also analysed two feature engineering procedures, the quadrat method and KDE in which we conduct a qualitative and quantitative evaluation to suggest which better translate geographic patterns of the city. Further, we provide some illustrative examples of Geohunter applications.

Paper Citation

in Harvard Style

de Araujo A., Marcos do Valle J. and Cacho N. (2020). Geographic Feature Engineering with Points-of-Interest from OpenStreetMap. In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 1: KDIR; ISBN 978-989-758-474-9, SciTePress, pages 116-123. DOI: 10.5220/0010155101160123

in Bibtex Style

@conference{kdir20,
author={Adelson de Araujo and João Marcos do Valle and Nélio Cacho},
title={Geographic Feature Engineering with Points-of-Interest from OpenStreetMap},
booktitle={Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 1: KDIR},
year={2020},
pages={116-123},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010155101160123},
isbn={978-989-758-474-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 1: KDIR
TI - Geographic Feature Engineering with Points-of-Interest from OpenStreetMap
SN - 978-989-758-474-9
AU - de Araujo A.
AU - Marcos do Valle J.
AU - Cacho N.
PY - 2020
SP - 116
EP - 123
DO - 10.5220/0010155101160123
PB - SciTePress