Conflating point of interest (POI) data: A systematic review of matching methods

Point of interest (POI) data provide digital representations of places in the real world, and have been increasingly used to understand human-place interactions, support urban management, and build smart cities. Many POI datasets have been developed, which often have different geographic coverages, attribute focuses, and data quality. From time to time, researchers may need to conflate two or more POI datasets in order to build a better representation of the places in the study areas. While various POI conflation methods have been developed, there lacks a systematic review, and consequently, it is difficult for researchers new to POI conflation to quickly grasp and use these existing methods. This paper fills such a gap. Following the protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we conduct a systematic review by searching through three bibliographic databases using reproducible syntax to identify related studies. We then focus on a main step of POI conflation, i.e., POI matching, and systematically summarize and categorize the identified methods. Current limitations and future opportunities are discussed afterwards. We hope that this review can provide some guidance for researchers interested in conflating POI datasets for their research

Concatenating Daily Exercise Routes with Public Sports Facilities, Bicycle Lanes, and Green Spaces: A Feasibility Analysis in Nanjing, China

Public sports facilities have the potential to improve their functions as active living infrastructures (ALIs) in combination with bicycle lanes and green spaces. A favorable sequence of exercise intensities in different scenes is important for individuals to take physical activity scientifically. Our research aimed to explore the feasibility of promoting and consolidating this sequence using reasonable daily exercise routes concatenated by public sports facilities, green spaces, and bicycle lanes. Taking 25 major public sports facilities in Nanjing as an example, we obtained the cycling routes from open-source data and delineated the facilities’ cycling catchment areas to assess the coordination of bicycle lanes and facilities. Further, we evaluated the potential interactions between facilities and green spaces by checking the spatial intersections between park entrances and the above routes. The results revealed that with the integration of bicycle lanes, public sports facilities could provide services to most residential areas, and potential interactions between the facilities and parks existed already. Therefore, it was feasible to design reasonable daily exercise routes coupled with the existing facility layout. Moreover, the service gaps and potential interactions were affected by the layout of the facilities, the density of the bicycle lanes, the configuration of green spaces, and the official planning proposals. This research advances the understanding of how public sports facilities can be pivotal to the cooperation of ALIs with other infrastructures

Concatenating Daily Exercise Routes with Public Sports Facilities, Bicycle Lanes, and Green Spaces: A Feasibility Analysis in Nanjing, China

Public sports facilities have the potential to improve their functions as active living infrastructures (ALIs) in combination with bicycle lanes and green spaces. A favorable sequence of exercise intensities in different scenes is important for individuals to take physical activity scientifically. Our research aimed to explore the feasibility of promoting and consolidating this sequence using reasonable daily exercise routes concatenated by public sports facilities, green spaces, and bicycle lanes. Taking 25 major public sports facilities in Nanjing as an example, we obtained the cycling routes from open-source data and delineated the facilities’ cycling catchment areas to assess the coordination of bicycle lanes and facilities. Further, we evaluated the potential interactions between facilities and green spaces by checking the spatial intersections between park entrances and the above routes. The results revealed that with the integration of bicycle lanes, public sports facilities could provide services to most residential areas, and potential interactions between the facilities and parks existed already. Therefore, it was feasible to design reasonable daily exercise routes coupled with the existing facility layout. Moreover, the service gaps and potential interactions were affected by the layout of the facilities, the density of the bicycle lanes, the configuration of green spaces, and the official planning proposals. This research advances the understanding of how public sports facilities can be pivotal to the cooperation of ALIs with other infrastructures

Deriving neighborhood-level diet and physical activity measurements from anonymized mobile phone location data for enhancing obesity estimation

Abstract Background Obesity is a serious public health problem. Existing research has shown a strong association between obesity and an individual’s diet and physical activity. If we extend such an association to the neighborhood level, information about the diet and physical activity of the residents of a neighborhood may improve the estimate of neighborhood-level obesity prevalence and help identify the neighborhoods that are more likely to suffer from obesity. However, it is challenging to measure neighborhood-level diet and physical activity through surveys and interviews, especially for a large geographic area. Methods We propose a method for deriving neighborhood-level diet and physical activity measurements from anonymized mobile phone location data, and examine the extent to which the derived measurements can enhance obesity estimation, in addition to the socioeconomic and demographic variables typically used in the literature. We conduct case studies in three different U.S. cities, which are New York City, Los Angeles, and Buffalo, using anonymized mobile phone location data from the company SafeGraph. We employ five different statistical and machine learning models to test the potential enhancement brought by the derived measurements for obesity estimation. Results We find that it is feasible to derive neighborhood-level diet and physical activity measurements from anonymized mobile phone location data. The derived measurements provide only a small enhancement for obesity estimation, compared with using a comprehensive set of socioeconomic and demographic variables. However, using these derived measurements alone can achieve a moderate accuracy for obesity estimation, and they may provide a stronger enhancement when comprehensive socioeconomic and demographic data are not available (e.g., in some developing countries). From a methodological perspective, spatially explicit models overall perform better than non-spatial models for neighborhood-level obesity estimation. Conclusions Our proposed method can be used for deriving neighborhood-level diet and physical activity measurements from anonymized mobile phone data. The derived measurements can enhance obesity estimation, and can be especially useful when comprehensive socioeconomic and demographic data are not available. In addition, these derived measurements can be used to study obesity-related health behaviors, such as visit frequency of neighborhood residents to fast-food restaurants, and to identify primary places contributing to obesity-related issues