ARTICLE What Is So ‘‘Hot’’ in Heatmap? Qualitative Code Cluster Analysis with Foursquare Venue Ilyoung Hong Department of GIS Engineering / Namseoul University / Cheonan-si Chungcheongnam-do / South Korea Jin-Kyu Jung School of Interdisciplinary Arts and Sciences / University of Washington-Bothell / Bothell / WA / USA ABSTRACT Foursquare is a popular Web service and a representative location-based social network (LBSN) service using position data. Heatmap is a widely used means of geovisualization for analyzing social data with locational values. Until now, heatmap analysis of LBSN has focused on identifying quantitative distribution and patterns, with little consideration of the qualitative analysis of data content. Based on a case study of Foursquare venues and user-created content in Seattle, WA, this study conducts analyses assessing both the quantitative spatial distribution and the qualitative characteristics of coffee shops in the Seattle metropolitan area. It specifically proposes a new analytical method referred to as ‘‘code cluster,’’ which is designed to employ quantitative and qualitative approaches simultaneously. The significance of this method is its capacity to explain geographical differences in terms of qualitative traits in cluster regions, in addition to analyzing their spatial characteristics and distributions. In introducing this new hybrid approach, our aims are to reflect the original intent and essence of the data throughout the research process and to make further efforts to analyze and interpret the contextualized meanings. This will be possible through integration of advanced spatial analysis, geovisuali- zation, and qualitative research that build on current geographic and geovisual research with big data. Keywords: code cluster, hybrid approach, geovisualization, spatial analysis, location-based social network, Foursquare RE ´ SUME ´ Foursquare est un service Web populaire, typique des re´seaux sociaux ge´ode´pendants (RSG) utilisant les donne´es de ge´olocalisation. La carte de densite´ de clics est une technique de ge´ovisualisation largement utilise´e pour analyser les donne´es sociales ayant des valeurs a` re´fe´rence spatiale. Jusqu’a` maintenant, l’analyse des cartes de densite´ de clics des RSG visait plus particulie`rement l’observation de distributions et de profils quantitatifs, l’analyse qualitative du contenu en information suscitant peu d’inte´reˆt. Dans une e´tude de cas portant sur des endroits de Foursquare et un contenu cre´e´ par les utilisateurs a` Seattle (Washington), les auteurs proce`dent a` des analyses visant a`e´valuer la distribution spatiale quantitative et les caracte´ristiques qualitatives des cafe´s-restaurants de la re´gion me´tropolitaine de Seattle. Ils proposent plus pre´cise´ment une nouvelle me´thode analytique dite des « grappes de codes », conc¸ue pour permettre l’emploi simul- tane´ d’approches quantitative et qualitative. L’inte´reˆt de cette me´thode tient au fait qu’elle peut expliquer les diffe´rences ge´ographiques en termes de caracte´ristiques qualitatives dans les re´gions accueillant une grappe, en plus de l’analyse des caracte´ristiques et des distributions spatiales. En proposant cette nouvelle me´thode hybride, les auteurs ont pour but de rendre compte de l’intention et de l’essence initiales des donne´es au fil du processus de recherche et de consacrer davantage d’efforts a` l’analyse et a` l’interpre´tation de leurs significations contextualise´es. Ce but pourra eˆtre atteint graˆce a` l’inte´gration de l’analyse spatiale avance´e, de la ge´ovisualisation et de la recherche qualitative reposant sur la recherche ge´ographique et ge´ovisuelle actuelle faisant appel aux me´gadonne´es. Mots cle´s : analyse spatiale, Foursquare, ge´ovisualisation, grappe de codes, me´thode hybride, re´seau social ge´ode´pendant Introduction Foursquare and Facebook Place are popular location-based social networks (LBSNs) that use position data and Web services. Unlike conventional social media such as Twitter and Facebook, LBSNs allow users to form relationships around particular locations. For example, users can save location data regarding places of interest and leave com- ments to share the information with their friends and other users. LBSN data have also become an important 332 Cartographica 52:4, 2017, pp. 332–348 6 University of Toronto Press doi:10.3138/cart.52.4.2016-0005 Cartographica: The International Journal for Geographic Information and Geovisualization 2017.52:332-348.