Incorporating spatial interaction patterns in classifying and understanding urban land use

Abstract

Land use classification has benefited from the emerging big data, such as mobile phone records and taxi trajectories. Temporal activity variations derived from these data have been used to interpret and understand the land use of parcels from the perspective of social functions, complementing the outcome of traditional remote sensing methods. However, spatial interaction patterns between parcels, which could depict land uses from a perspective of connections, have rarely been examined and analysed. To leverage spatial interaction information contained in the above-mentioned massive data sets, we propose a novel unsupervised land use classification method with a new type of place signature. Based on the observation that spatial interaction patterns between places of two specific land uses are similar, the new place signature improves land use classification by trading off between aggregated temporal activity variations and detailed spatial interactions among places. The method is validated with a case study using taxi trip data from Shanghai. ARTICLE HISTORY

Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

“…A similar approach that fuses remote sensing and various social sensing data can be used to obtain a land use map. When fusion is performed, a key step is to aggregate the social data into a certain spatial unit, and different aggregation methods might be used for these various social sensing data [25,40,42,43]. For the POIs data, spatial distribution density of POIs by category is the prevalent way to express social features with a certain spatial unit [30].…”

“…For the mobile phone data, the volume density in spatial units is often aggregated via spatial interpolation according to the location and volume of base transceiver stations [25]. For the trajectory data, not only the volume of inflow and outflow of periods, but also the connections between places are factors that should be taken into consideration to aggregate the data [42]. These extracted social features can then be fused with the physical features derived from remote sensing data to produce the final land use map.…”

“…Several alternative sources of social sensing data, including POIs [30], check-in data with location information [40,41], mobile phone data [25], and trajectory data [42], can then be applied to replace the MPPD data. A similar approach that fuses remote sensing and various social sensing data can be used to obtain a land use map.…”

Investigating Public Facility Characteristics from a Spatial Interaction Perspective: A Case Study of Beijing Hospitals Using Taxi Data

“…We can classify a set of places based on their spatial interactions. Following this idea, Liu et al [16] incorporated spatial interaction patterns into land use classification using an unsupervised method with taxi data from Shanghai and achieved better results.…”

“…With the support of big geo-data, we collect a massive volume of data on human movement and flow to quantify spatial interactions between places [12]. Numerous studies have been conducted to examine urban or regional structures using spatial interaction information extracted from social media data [13], phone call records [14], public transportation card records [15], and taxi trajectories [16]. In these studies, community detection methods, which are borrowed from network sciences, were widely adopted to delineate a study area into meaningful sub-regions according to interaction strengths.…”