Points of Interest (POI): a commentary on the state of the art, challenges, and prospects for the future

In this commentary, we describe the current state of the art of points of interest (POIs) as digital, spatial datasets, both in terms of their quality and affordings, and how they are used across research domains. We argue that good spatial coverage and high-quality POI features — especially POI category and temporality information — are key for creating reliable data. We list challenges in POI geolocation and spatial representation, data fidelity, and POI attributes, and address how these challenges may affect the results of geospatial analyses of the built environment for applications in public health, urban planning, sustainable development, mobility, community studies, and sociology. This commentary is intended to shed more light on the importance of POIs both as standalone spatial datasets and as input to geospatial analyses

Site Selection Using Geo-Social Media: A Study For Eateries In Lisbon

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe rise in the influx of multicultural societies, studentification, and overall population growth has positively impacted the local economy of eateries in Lisbon, Portugal. However, this has also increased retail competition, especially in tourism. The overall increase in multicultural societies has also led to an increase in multiple smaller hotspots of human-urban attraction, making the concept of just one downtown in the city a little vague. These transformations of urban cities pose a big challenge for upcoming retail and eateries store owners in finding the most optimal location to set up their shops. An optimal site selection strategy should recommend new locations that can maximize the revenues of a business. Unfortunately, with dynamically changing human-urban interactions, traditional methods like relying on census data or surveys to understand neighborhoods and their impact on businesses are no more reliable or scalable. This study aims to address this gap by using geo-social data extracted from social media platforms like Twitter, Flickr, Instagram, and Google Maps, which then acts as a proxy to the real population. Seven variables are engineered at a neighborhood level using this data: business interest, age, gender, spatial competition, spatial proximity to stores, homogeneous neighborhoods, and percentage of the native population. A Random Forest based binary classification method is then used to predict whether a Point of Interest (POI) can be a part of any neighborhood n. The results show that using only these 7 variables, an F1-Score of 83% can be achieved in classifying whether a neighborhood is good for an “eateries” POI. The methodology used in this research is made to work with open data and be generic and reproducible to any city worldwide

Regionalization of Social Interactions and Points-of-Interest Location Prediction with Geosocial Data

Traditional methods for studying the activity dynamics of people and their social interactions in cities require time-consuming and resource-intensive observations and surveys. Dynamic online trails from geosocial networks (e.g. Twitter, Instagram, Flickr etc.) have been increasingly used as proxies for human activity, focusing on mobility behavior, spatial interaction, and social connectivity, among others. Social media records incorporate geo-tags, timestamps, textual components, user-profile attributes and points-ofinterest (POI) features, which respectively address spatial, temporal, topical, demographic, and contextual dimensions of human activity. While the information contained in social media data is complex and highdimensional, there is a lack of studies exploiting the combined potential of their information layers. This article introduces a framework that considers multiple dimensions (i.e. spatial, temporal, topical, and demographic) of information from social media data, and combines Geo-Self-Organizing Maps (GeoSOMs) in conjunction with contiguity-constrained hierarchical clustering, to identify homogeneous regions of social interaction in cities and, subsequently, estimate appropriate locations for new POIs. Drawing on the discovered regions, we build a Factorization Machine-based model to estimate appropriate locations for new POIs in different urban contexts. Using geo-referenced Twitter records and Foursquare data from Amsterdam, Boston, and Jakarta, we evaluate the potential of machine learning techniques in discovering knowledge about the geography of social dynamics from unstructured and high-dimensional social web data. Moreover, we demonstrate that the discovered homogeneous regions are significant predictors of new POI locations.Web Information System