Spotting Premium Hot Spots for Urban Tourism Based on Facebook and Foursquare Data Using VGI and GIS

Spotting Premium Hot Spots for Urban Tourism Based on Facebook and Foursquare Data Using VGI and GIS

José Gomes dos Santos (University of Coimbra, Portugal), Liliana Raquel Simões Azevedo (University of Coimbra, Portugal) and Luís Carlos Roseiro Leitão (University of Coimbra, Portugal)
DOI: 10.4018/978-1-7998-2249-3.ch006
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Spatial modeling always involves choices. The existence of constraints, the uncertainty and even the reliability of the data, the purposes and the applications of the studies make these reflections a kind of guiding compass for GIS analysts. Building on a previous exercise of data acquisition (check-ins) based on two digital social networks (DSN – Facebook and Foursquare) and on the awareness of the use of volunteered geographic information (VGI) generated by tourists through DSN, this work aims to evaluate the contribution of spatial analysis applied to urban tourism in the “Alta and University of Coimbra” area. Concepts and procedural tasks related to density determination, cluster analysis, and identification of patterns have thus been implemented with the purpose of evaluating and comparing the results obtained through the application of two techniques of spatial analysis, kernel density estimation (KDE) and optimized hot spot analysis (OHSA) and inverse distance weighting (IDW) interpolation.
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Working with spatial models and Geographical Information Systems (GIS) involves choices. There are always constraints, either of a financial nature or related to the specifics of the software itself, to the algorithms being used, to the level of uncertainty or to the contingencies that affect the consistency and accuracy of the data. Spatial modeling encompasses all these components, and the researchers should be aware of this fact if they wish to be able to discuss the best solutions for each case. To these initial thoughts one should add the words of George E. P. Box and Norman Richard Draper when they wrote in 1987: “Remember that all models are wrong; the practical question is how wrong they have to be to not be useful. Essentially, all models are wrong, but some are useful” (Rocha, 2012, 323).

Geostatistical methodologies, modeling and spatial analysis are currently used in such varied fields as the communication strategies of numerous economic and scientific/research activities, both in the state and private sector, and applied to many different branches of knowledge, from the biomedical to the spatial sciences and, of course, the geosciences. They resort to a set of tools that allow for the construction of geospatial models and the inclusion of predictive capabilities conducive to the shaping of a system aimed at explaining both natural and social phenomena.

The present study builds on a previous broader research project that sought to compare and assess the reliability of three geostatistical algorithms, two of which in association (complement), applied to the study of urban tourism in the “Alta and University of Coimbra”, the uptown area and University of Coimbra main campus, in Portugal’s Centre region.

Key Terms in this Chapter

Spatial Analysis: Refers to statistical analysis based on patterns and spatial relations between variables, human and/or natural.

Optimized Hot Spot Analysis: Given incident points or weighted features (points or polygons), Optimized Hot Spot Analysis creates a map of statistically significant hot and cold spots using the Getis-Ord Gi* statistic (Source: ArcGIS-ESRI).

Inverse Distance Weighted: Is an interpolation method which “explicitly, makes the assumption that things that are close to one another are more alike than those that are farther apart. To predict a value for any unmeasured location, IDW uses the measured values surrounding the prediction location. The measured values closest to the prediction location have more influence on the predicted value than those farther away. IDW assumes that each measured point has a local influence that diminishes with distance. It gives greater weights to points closest to the prediction location, and the weights diminish as a function of distance, hence the name inverse distance weighted” (Source:

Volunteered Geographical Information: The term was coined by Michael Goodchild (2007) to describe “an explosion of interest in using the Web to create, assemble, and disseminate geographic information provided voluntarily by individuals.” VGI is the spatial subset of user-generated content appearing at that time tied to the advent of Web 2.0 technologies (Source: Encyclopedia of GIS, 2017).

Digital Social Network: Online platform-based services which people use to communicate and to exchange various types of content, from Data and Information to Knowledge and Intelligence. They are multi-purpose platforms, which can be categorized according to the main purposes of sharing information.

Kernel Density Estimation: Is a mathematic process of finding an estimate probability density function of a random variable. The estimation attempts to infer characteristics of a population, based on a finite data set. It is a powerful way to estimate probability density (Source:

Geographic Information Systems: Is a framework for gathering, managing, and analyzing data. Rooted in the science of geography, GIS integrates many types of data. It analyzes spatial location and organizes layers of information into visualizations using maps and 3D scenes. With this unique capability, GIS reveals deeper insights into data, such as patterns, relationships, and situations - helping users make smarter decisions (Source:

Urban Tourism: According to the World Tourism Organization (UNWTO), urban tourism is defined as “a type of tourism activity which takes place in an urban space with its inherent attributes characterized by non-agricultural based economy such as administration, manufacturing, trade and services and by being nodal points of transport (Source:

Geostatistics: Is a branch of Statistic Sciences focusing on spatial relations and analysis and/or in the spatiotemporal datasets. Geostatistics involves regionalized variables and some algorithms are incorporated in Geographical Information Systems (GIS). Formally, according to Matheron (1965) “Geostatistics is the application of the formalism of Random Functions to the reconnaissance and estimation of natural phenomena.”

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