# Human Mobility and Location-Based Analysis

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/human-mobility-and-location-based-analysis/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on understanding human mobility patterns using various sources of data such as GPS, mobile phone, and smart card data. It explores topics related to transportation modes, urban analysis, population movements, and travel behavior. The research aims to uncover the spatial and temporal dynamics of human mobility at both individual and collective levels. |
| Domain | Social Sciences |
| Field | Social Sciences |
| OpenAlex ID | t11980 |
| Works | 67 |

## Topic papers all

Showing 15 of 67.

- [A survey of mobile phone sensing](https://scholariq.org/papers/a-survey-of-mobile-phone-sensing/)
- [Graph neural network for traffic forecasting: A survey](https://scholariq.org/papers/graph-neural-network-for-traffic-forecasting-a-survey/)
- [Smart Cities and the Future Internet: Towards Cooperation Frameworks for Open Innovation](https://scholariq.org/papers/smart-cities-and-the-future-internet-towards-cooperation-frameworks-for-open/)
- [The Jigsaw continuous sensing engine for mobile phone applications](https://scholariq.org/papers/the-jigsaw-continuous-sensing-engine-for-mobile-phone-applications/)
- [Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States](https://scholariq.org/papers/using-deep-learning-and-google-street-view-to-estimate-the-demographic-makeup-of/)
- [A hybrid discriminative/generative approach for modeling human activities](https://scholariq.org/papers/a-hybrid-discriminative-generative-approach-for-modeling-human-activities/)
- [GPS tracking in neighborhood and health studies: A step forward for environmental exposure assessment, a step backward for causal inference?](https://scholariq.org/papers/gps-tracking-in-neighborhood-and-health-studies-a-step-forward-for-environmental/)
- [Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation](https://scholariq.org/papers/where-to-go-next-a-spatio-temporal-gated-network-for-next-poi-recommendation/)
- [Luxembourg SUMO Traffic (LuST) Scenario: 24 hours of mobility for vehicular networking research](https://scholariq.org/papers/luxembourg-sumo-traffic-lust-scenario-24-hours-of-mobility-for-vehicular/)
- [Mobility Detection Using Everyday GSM Traces](https://scholariq.org/papers/mobility-detection-using-everyday-gsm-traces/)
- [Smartphone sensing methods for studying behavior in everyday life](https://scholariq.org/papers/smartphone-sensing-methods-for-studying-behavior-in-everyday-life/)
- [Adapting to User Interest Drift for POI Recommendation](https://scholariq.org/papers/adapting-to-user-interest-drift-for-poi-recommendation/)
- [A deep learning model for air quality prediction in smart cities](https://scholariq.org/papers/a-deep-learning-model-for-air-quality-prediction-in-smart-cities/)
- [Event classification and location prediction from tweets during disasters](https://scholariq.org/papers/event-classification-and-location-prediction-from-tweets-during-disasters/)
- [The Rising Role of Big Data Analytics and IoT in Disaster Management: Recent Advances, Taxonomy and Prospects](https://scholariq.org/papers/the-rising-role-of-big-data-analytics-and-iot-in-disaster-management-recent/)

## Topic primary papers

Showing 15 of 18.

- [Smartphone sensing methods for studying behavior in everyday life](https://scholariq.org/papers/smartphone-sensing-methods-for-studying-behavior-in-everyday-life/)
- [Adapting to User Interest Drift for POI Recommendation](https://scholariq.org/papers/adapting-to-user-interest-drift-for-poi-recommendation/)
- [A deep learning model for air quality prediction in smart cities](https://scholariq.org/papers/a-deep-learning-model-for-air-quality-prediction-in-smart-cities/)
- [Event classification and location prediction from tweets during disasters](https://scholariq.org/papers/event-classification-and-location-prediction-from-tweets-during-disasters/)
- [The Rising Role of Big Data Analytics and IoT in Disaster Management: Recent Advances, Taxonomy and Prospects](https://scholariq.org/papers/the-rising-role-of-big-data-analytics-and-iot-in-disaster-management-recent/)
- [Cellular traffic prediction with machine learning: A survey](https://scholariq.org/papers/cellular-traffic-prediction-with-machine-learning-a-survey/)
- [Evaluating Variable-Length Markov Chain Models for Analysis of User Web Navigation Sessions](https://scholariq.org/papers/evaluating-variable-length-markov-chain-models-for-analysis-of-user-web/)
- [Exploring urban tourism crowding in Shanghai via crowdsourcing geospatial data](https://scholariq.org/papers/exploring-urban-tourism-crowding-in-shanghai-via-crowdsourcing-geospatial-data/)
- [Visualization of Urban Mobility Data from Intelligent Transportation Systems](https://scholariq.org/papers/visualization-of-urban-mobility-data-from-intelligent-transportation-systems/)
- [Enhancing pedestrian mobility in Smart Cities using Big Data](https://scholariq.org/papers/enhancing-pedestrian-mobility-in-smart-cities-using-big-data/)
- [Traffic forecasting in cellular networks using the LSTM RNN](https://scholariq.org/papers/traffic-forecasting-in-cellular-networks-using-the-lstm-rnn/)
- [Segmenting human trajectory data by movement states while addressing signal loss and signal noise](https://scholariq.org/papers/segmenting-human-trajectory-data-by-movement-states-while-addressing-signal-loss/)
- [Indoor Location Data for Tracking Human Behaviours: A Scoping Review](https://scholariq.org/papers/indoor-location-data-for-tracking-human-behaviours-a-scoping-review/)
- [Development of a Global Positioning System Web-Based Prompted Recall Solution for Longitudinal Travel Surveys](https://scholariq.org/papers/development-of-a-global-positioning-system-web-based-prompted-recall-solution/)
- [Greater Kuala Lumpur as a smart city: A case study on technology opportunities](https://scholariq.org/papers/greater-kuala-lumpur-as-a-smart-city-a-case-study-on-technology-opportunities/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.