Fig 4
![Map representation of Strava intensity of use and Google Maps activities for (a) the El Carrilet Olot-Girona data analysis; and, (b), the Alcoi-Alicante data analysis [23].](profile/Almudena_Nolasco-Cirugeda/publication/364367449/figure/fig2/AS:11431281090804336@1666189962491/Map-representation-of-Strava-intensity-of-use-and-Google-Maps-activities-for-a-the-El.jpg)
Map representation of Strava intensity of use and Google Maps activities for (a) the El Carrilet Olot-Girona data analysis; and, (b), the Alcoi-Alicante data analysis [23].
Source publication
Location-Based Social Networks data —LBSN data— reveal, in essence, user preferences and patterns of use of urban space. This information plays a key role in research on social dynamics in cities. Today, social network applications are widely available and this digital data represents a complementary and inescapable source of data for the analysis...
Contexts in source publication
Context 1
... interest in activities and a great intensity of use were identified in the surroundings and in continuity of the greenway's layout. Most of the facilities and services -retrieved from Google Maps-are located on intensivelyused streets, according to Strava Heatmap data - Fig. 4-. This proves that these peri-urban areas are socially significant spaces in the city. Firstly, in the case of El Carrilet greenway, Wikiloc registers show a total of 2102 routes for the whole city. Some 16% of the total sample -169 routes-are labelled "greenway Carrilet Olot". Considering only the paths in the city's peri-urban area ...
Context 2
... routes, representing 42.37%, have been inventoried in the peri-urban area. These figures show significant activity concentration differences with respect to El Carrilet. A possible reason may be the fact that Alcoi presents a larger concentration of uses and activities, as well as a better interconnection of nodes of structural urban facilities - Fig. ...
Citations
... The data on such platforms typically include user check-in records, review content, social relationships, and geographical location information, providing crucial support for various research and practical applications. The application scenarios of LBSNs are broad, covering areas such as POI recommendation [27][28][29], friend recommendation [30][31][32], spatiotemporal analysis [33,34], social interaction [35,36], and privacy protection [37][38][39]. In the context of friend recommendation, identifying potential friend relationships from massive data has become a critical issue that needs to be addressed. ...
Friend link prediction is an important issue in recommendation systems and social network analysis. In Location-Based Social Networks (LBSNs), predicting potential friend relationships faces significant challenges due to the diversity of user behaviors, along with the high dimensionality, sparsity, and complex noise in the data. To address these issues, this paper proposes a Heterogeneous Graph Attention Network (GEVEHGAN) model based on Lite Gate Recurrent Unit (Lite-GRU) embedding and Variational Autoencoder (VAE) enhancement. The model constructs a heterogeneous graph with two types of nodes and three types of edges; combines Skip-Gram and Lite-GRU to learn Point of Interest (POI) and user node embeddings; introduces VAE for dimensionality reduction and denoising of the embeddings; and employs edge-level attention mechanisms to enhance information propagation and feature aggregation. Experiments are conducted on the publicly available Foursquare dataset. The results show that the GEVEHGAN model outperforms other comparative models in evaluation metrics such as AUC, AP, and Top@K accuracy, demonstrating its superior performance in the friend link prediction task.
... It also identifies the main thematic lines of investigation based on the data provided by these platforms to offer a framework for studying urban phenomena through LBSN data. Nolasco-Cirugeda and García-Mayor [2022] show how data from Foursquare, Twitter, and Google Places are crucial for analyzing the use of urban space and social dynamics. By detailing pioneering research and case studies over the last decade, the article shows how LBSN data offer important information about urban life, helping to understand social dynamics and specific urban interventions. ...
Location-Based Social Networks (LBSNs) are valuable for understanding urban behavior and providing useful data on user preferences. Modeling their data into graphs like interest networks (iNETs) offers important insights for urban area recommendations, mobility forecasting, and public policy development. This study uses check-ins and venue reviews to compare the iNETs resulting from two distinct LBSNs, Foursquare and Google Places. Although these two LBSNs differ in nature, with data varying in regularity and purpose, their resulting iNETs reveal similar urban behavior patterns. When analyzing the impact of socioeconomic, political, and geographic factors on iNET edges-each edge representing users' interests in a pair of regions-only geographic factors showed a significant influence. When studying the granularity of area sizes to model iNETs, we highlight important trade-offs between larger and smaller sizes. Additionally, we propose a methodology to identify clusters of geographically neighboring areas where user interest is strongest, which can be advantageous for understanding urban space usage.
... Numa avaliação comparativa entre várias LBSNs, tanto os benefícios das plataformas para o estudo dos fenômenos urbanos quanto os desafios envolvidos foram reconhecidos em [11]. Já [12] evidencia os diversos cenários em que os dados são úteis, como, por exemplo, na compreensão da dinâmica social, em intervenções urbanas específicas, no turismo, no papel da infraestrutura verde, entre outros. Buscando entender se diferentes LBSNs podem ser utilizadas juntas para o entendimento urbano, [21] usa o Instagram e o Foursquare para mostrar que um check-in do Foursquare pode trazer informação sobre a categoria de um estabelecimento comentado em uma publicação do Instagram. ...
Location-Based Social Networks (LBSNs) can help model users’ interests in urban areas in several ways. In the present work, we focus on Interest Networks (iNETs), which result from modeling LBSN data into graphs. The present study provides insights into which areas are frequently visited together by getting data from two distinct LBSNs, Foursquare and Google Places. Although the studied LBSNs differ in nature, with data varying in regularity and purpose, both modeled iNETs revealed similar urban behavior patterns and were likewise impacted by socioeconomic and geographic factors. Also, we discuss the development of a tool to empower urban studies and the by-products of this research.
... For instance, reference [49] mapped user behaviors and sentiments in New York using geo-coded Twitter data. Although the data sources and underlying algorithms were not disclosed, references [50][51][52] used the Instasights heat map, which is a web-based social media mapping tool, to monitor the impact of renewed waterfront areas in Spanish cities. ...
The image of a city represents the sum of beliefs, ideas, and impressions that people have of that city. Mostly, city images are assessed through direct or indirect interviews and cognitive mapping exercises. Such methods consume more time and effort and are limited to a small number of people. However, recently, people tend to use social media to express their thoughts and experiences of a place. Taking this into consideration, this paper attempts to explore city images through social media big data, considering Colombo, Sri Lanka, as the testbed. The aim of the study is to examine the image of a city through Lynchian elements—i.e., landmarks, paths, nodes, edges, and districts—by using community sentiments expressed and images posted on social media platforms. For that, this study conducted various analyses—i.e., descriptive, image processing, sentiment, popularity, and geo-coded social media analyses. The study findings revealed that: (a) the community sentiments toward the same landmarks, paths, nodes, edges, and districts change over time; (b) decisions related to locating landmarks, paths, nodes, edges, and districts have a significant impact on community cognition in perceiving cities; and (c) geo-coded social media data analytics is an invaluable approach to capture the image of a city. The study informs urban authorities in their placemaking efforts by introducing a novel methodological approach to capture an image of a city.
As Redes Sociais Baseadas em Localização (LBSNs) são úteis na compreensão do comportamento urbano, oferecendo dados valiosos sobre preferências dos usuários. A modelagem desses dados em grafos, como as Redes de Interesse, permite percepções relevantes. Essas redes podem ser úteis para, por exemplo, recomendações de áreas urbanas, previsões de mobilidade e formulação de políticas públicas. Este estudo compara redes de interesse de duas LBSNs distintas, Foursquare e Google Places, usando dados de check-ins e avaliações de estabelecimentos. Embora as LBSNs estudadas sejam diferentes em natureza, com dados diferindo em regularidade e propósito, ambas as redes de interesse modeladas revelaram padrões similares de comportamento urbano. Fatores socioeconômicos e geográficos também mostraram impacto semelhante nas redes de interesse estudadas.
