Land Use detection with cell phone data using topic models: Case Santiago, Chile

Ríos, Sebastián A.; Munoz, Ricardo

doi:10.1016/j.compenvurbsys.2016.08.007

Cited by 55 publications

(47 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Using a similar methodology, Zhan et al (2014) inferred four broad categories of land use based on a spatiotemporal analysis of Twitter data: residential, retail, open space/recreation and transportation/utility. A similar approach has been adopted in other studies (e.g., Pei et al, 2014;Ríos and Muñoz, 2017), in which clustering algorithms have been used to group city zones with similar profiles according to mobile phone activity, or Chen et al (2017) in China using the social media "Tencent". These methodologies based on clustering the tweeting activity profiles of different areas in the city could be used as an alternative to satellite imagery, to infer urban land uses based on social network data.…”

Section: Human Activities and Land Uses: Spatiotemporal Patterns Of Dmentioning

confidence: 99%

City dynamics through Twitter: Relationships between land use and spatiotemporal demographics

Palomares

Salas-Olmedo

Moya-Gómez

et al. 2018

Cities

109

View full text Add to dashboard Cite

Section: Human Activities and Land Uses: Spatiotemporal Patterns Of Dmentioning

confidence: 99%

City dynamics through Twitter: Relationships between land use and spatiotemporal demographics

Palomares

Salas-Olmedo

Moya-Gómez

et al. 2018

Cities

109

View full text Add to dashboard Cite

“…In addition, relational data in terms of directionality of calling and texting between users can be used to infer social networks in cyberspace (Kang, Zhang, Ma, & Liu, ) or detect community boundaries (Gao, Liu, Wang, & Ma, ; Sevtsuk & Ratti, ; Shi, Chi, Liu, & Liu, ). Variations in aggregated call volumes at different server towers across time can be used to study location characteristics and infer urban land use types and patterns (Pei et al, ; Ríos & Muñoz, ). This land use information can further contribute to innovative understanding of urban spatial structures and planning (Louail et al, ).…”

Section: Resultsmentioning

confidence: 99%

Big data‐enabled social sensing in spatial analysis: Potentials and pitfalls

Liao

Brown

Ding

et al. 2018

Transactions in GIS

View full text Add to dashboard Cite

Over the past decade, there has been an unprecedented boom in the application of big data derived from location‐aware technology in GIScience. However, reflections on data quality lead to concerns about inaccurate or even erroneous spatial analysis results. This article contributes a meta‐analysis of big data‐enabled social sensing in spatial analysis. Big data‐enabled research has substantially enhanced our understanding of human population distribution and dynamics, land use, travel behavior, movement patterns, and spatial social networks from city to country to global levels. However, the effectiveness of big data‐enabled social sensing can be undermined by five critical issues related to research design: data volume, spatial coverage, locational information quality, and representativeness across socioeconomic and demographic groups. We suggest a framework for addressing the inherent bias in spatial big data at multiple stages of the research lifecycle and applying them to appropriate topics and scales of applications.

show abstract

“…Rios and Muñoz used a big mobile phone data set with 880 million records in a case study for Santiago, Chile for land use pattern detection. They used the latent variable clustering technique in detecting clusters of residential, office area, leisure-commerce and rush hour pattern areas [28]. Pei et al used hourly relative pattern and the total call volume trough semi supervised fuzzy c-means clustering approach in inferring land use types in Singapore, showing that the accuracy decreased with the increase in heterogeneity of land use and density of cell phone towers [6].…”

Section: Related Workmentioning

confidence: 99%

Uncovering the Relationship between Human Connectivity Dynamics and Land Use

Novovic

Brdar

Mesaroš

et al. 2020

IJGI

View full text Add to dashboard Cite

CDR (Call Detail Record) data are one type of mobile phone data collected by operators each time a user initiates/receives a phone call or sends/receives an sms. CDR data are a rich geo-referenced source of user behaviour information. In this work, we perform an analysis of CDR data for the city of Milan that originate from Telecom Italia Big Data Challenge. A set of graphs is generated from aggregated CDR data, where each node represents a centroid of an RBS (Radio Base Station) polygon, and each edge represents aggregated telecom traffic between two RBSs. To explore the community structure, we apply a modularity-based algorithm. Community structure between days is highly dynamic, with variations in number, size and spatial distribution. One general rule observed is that communities formed over the urban core of the city are small in size and prone to dynamic change in spatial distribution, while communities formed in the suburban areas are larger in size and more consistent with respect to their spatial distribution. To evaluate the dynamics of change in community structure between days, we introduced different graph based and spatial community properties which contain latent footprint of human dynamics. We created land use profiles for each RBS polygon based on the Copernicus Land Monitoring Service Urban Atlas data set to quantify the correlation and predictivennes of human dynamics properties based on land use. The results reveal a strong correlation between some properties and land use which motivated us to further explore this topic. The proposed methodology has been implemented in the programming language Scala inside the Apache Spark engine to support the most computationally intensive tasks and in Python using the rich portfolio of data analytics and machine learning libraries for the less demanding tasks.

show abstract

Land Use detection with cell phone data using topic models: Case Santiago, Chile

Cited by 55 publications

References 12 publications

City dynamics through Twitter: Relationships between land use and spatiotemporal demographics

City dynamics through Twitter: Relationships between land use and spatiotemporal demographics

Big data‐enabled social sensing in spatial analysis: Potentials and pitfalls

Uncovering the Relationship between Human Connectivity Dynamics and Land Use

Contact Info

Product

Resources

About