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Demand driven store site selection via multiple spatial-temporal data
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Demand driven store site selection via multiple spatial-temporal data (2016)Demand driven store site selection via mu…Trade area analysis using user generated mobile location data (2013)Trade area analysis using use…Geo-spotting (2013)Geo-spottingThe generalized maximal covering location problem (2002)The generalized maximal cover…The art and science of retail location decisions (2000)The art and science of retail…Research and Modeling of Commercial Location Selection Based on Geographic Big Data and Mobile Signaling Data—A Case Study of the Central Urban Area of Beijing (2024)Research and Modeling of Comm…Improving Land Use Classification using Human Mobility-based Hierarchical Place Embeddings (2021)Improving Land Use Classifica…Venue-Popularity Prediction Using Social Data Participatory Sensing Systems and RNNs (2020)Venue-Popularity Prediction U…NCF: A Neural Context Fusion Approach to Raw Mobility Annotation (2020)NCF: A Neural Context Fusion …Learning Fine Grained Place Embeddings with Spatial Hierarchy from Human Mobility Trajectories (2020)Learning Fine Grained Place E…Information Fusion for (Re)Configuring Bike Station Networks With Crowdsourcing (2020)Information Fusion for (Re)Co…Enabling Finer Grained Place Embeddings using Spatial Hierarchy from Human Mobility Trajectories (2020)Enabling Finer Grained Place …Comparative analysis of models and performance indicators for optimal service facility location (2020)Comparative analysis of model…Where to Build New Public Toilets? Multi-Source Urban Data Tell the Truth (2019)Where to Build New Public Toi…City2City (2019)City2City(Re)Configuring Bike Station Network via Crowdsourced Information Fusion and Joint Optimization (2018)(Re)Configuring Bike Station …Understanding individual human mobility patterns (2008)Limits of Predictability in Human Mobility (2010)Building the Multi-Modal Storytelling of Urban Emergency Events Based on Crowdsensing of Social Media Analytics (2016)Evaluating Trade Areas Using Social Media Data with a Calibrated Huff Model (2016)Exploring Millions of Footprints in Location Sharing Services (2021)Scoping out urban areas of tourist interest though geolocated social media data: Bucharest as a case study (2022)The Selection of Retail Locations (1959)Methods for Determining Store Trade Areas, Market Penetration, and Potential Sales (1966)Small town shopping decline: dependence and inconvenience for the disadvanged (1995)Multi‐outlet retail site location assessment (2003)The retail site location decision process using GIS and the analytical hierarchy process (2013)Marketing analytics: Methods, practice, implementation, and links to other fields (2018)Defining and Estimating a Trading Area (1964)THE MAXIMAL COVERING LOCATION PROBLEM (1974)Discrete location theory (1991)The gradual covering decay location problem on a network (2003)Determining Optimal Police Patrol Areas with Maximal Covering and Backup Covering Location Models (2007)Generalized coverage: New developments in covering location models (2009)Bike-sharing: History, Impacts, Models of Provision, and Future (2009)Methodologies for Cross-Domain Data Fusion: An Overview (2015)Trees Grow on Money: Urban Tree Canopy Cover and Environmental Justice (2015)E-Sharing: Data-driven Online Optimization of Parking Location Placement for Dockless Electric Bike Sharing (2020)Station Correlation Attention Learning for Data-driven Bike Sharing System Usage Prediction (2020)Region Representation Learning via Mobility Flow (2017)Representing Urban Functions through Zone Embedding with Human Mobility Patterns (2018)Geo-Tile2Vec: A Multi-Modal and Multi-Stage Embedding Framework for Urban Analytics (2022)Human Mobility Identification by Deep Behavior Relevant Location Representation (2022)Modeling Bike Share Station Activity: Effects of Nearby Businesses and Jobs on Trips to and from Stations (2015)Interblock Flow Prediction With Relation Graph Network for Cold Start on Bike-Sharing System (2022)Human mobility: Models and applications (2018)Exploiting User Preference and Mobile Peer Influence for Human Mobility Annotation (2020)Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis (2006)Multi-View Graph Contrastive Learning for Urban Region Representation (2023)Urban region representation learning with human trajectories: a multi-view approach incorporating transition, spatial, and temporal perspectives (2024)How Accessibility Shapes Land Use (1959)Scenarios and Policy Aggregation in Optimization Under Uncertainty (1991)Discovering regions of different functions in a city using human mobility and POIs (2012)Bike sharing station placement leveraging heterogeneous urban open data (2015)Point-of-Interest Demand Modeling with Human Mobility Patterns (2017)Urban Computing Leveraging Location-Based Social Network Data (2019)
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References

  • 2002The generalized maximal covering location problem266 cited
  • 2013Geo-spotting208 cited
  • 2000The art and science of retail location decisions202 cited
  • 2013Trade area analysis using user generated mobile location data72 cited

Cited by

  • 2020Comparative analysis of models and performance indicators for optimal service facility location29 cited
  • 2018(Re)Configuring Bike Station Network via Crowdsourced Information Fusion and Joint Optimization15 cited
  • 2020Enabling Finer Grained Place Embeddings using Spatial Hierarchy from Human Mobility Trajectories15 cited
  • 2019City2City10 cited
  • 2020NCF: A Neural Context Fusion Approach to Raw Mobility Annotation9 cited
  • 2020Information Fusion for (Re)Configuring Bike Station Networks With Crowdsourcing8 cited
  • 2020Venue-Popularity Prediction Using Social Data Participatory Sensing Systems and RNNs5 cited
  • 2020Learning Fine Grained Place Embeddings with Spatial Hierarchy from Human Mobility Trajectories5 cited
  • 2024Research and Modeling of Commercial Location Selection Based on Geographic Big Data and Mobile Signaling Data—A Case Study of the Central Urban Area of Beijing4 cited
  • 2019Where to Build New Public Toilets? Multi-Source Urban Data Tell the Truth4 cited

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  • 2021Improving Land Use Classification using Human Mobility-based Hierarchical Place Embeddings3 cited