Geospatial big data for urban planning and urban management

The recent ten years witnessed the great achievements on rich applications of Geospatial Big Data across a variety of disciplines. For example, a huge number of Landsat images are utilized in mapping high-resolution global forest cover and the global forest changes in the twenty-first century are explored (Hansen et al. 2013), which is impossible without the support of geospatial big data and the related automatic processing techniques. Based on the huge enterprise registration data in China, the economic and social development situations and trends are revealed by the non-statistic data and novel approaches (Li et al. 2018). City-wide fine-grained urban population distribution at building level is achieved by integrating and fusing multisource geospatial big data (Yao et al. 2017), which is usually not desired in traditional research. Geospatial Big Data provides a new transforming paradigm of scientific research especially at the crossroads of broad disciplines, including but not limited to the humanities, the physical sciences, engineering, and so on.

With the global trends of urbanization, no matter in developed or developing countries as well as under-developed countries and regions, population, energy, goods, and materials are all gradually aggregated in urban areas. Urban Planning and Urban Management (UPUM) becomes more and more critical for achieving the sustainable development goals (SDGs) set forward by the United Nation. Utilizing massive location-based geospatial big data emerges as a research frontier in both geospatial community and urban studies.

The Sixteenth International Conference on Computers in Urban Planning and Urban Management (CUPUM), held during July 8–12, 2019, in Wuhan (China), aims to address a diverse range of social and environmental issues that would affect urban planning and development based on computing technology. More than 300 quality research papers were received with various topics, such as agent-based modeling, simulation, spatial statistics, computer-aided design, land use, and transportation modeling. If we look at these papers from the dimension of data involvement, many of them focus on geospatial big data-driven UPUM studies. The geospatial big data utilized in these papers come from many resources, including satellite remote sensing, ubiquitous location-aware sensors in urban area, as well as social media data such as check-in data, taxi trajectories, Weibo, and short message. Many interesting results are presented in these papers with new perspectives, methodologies, scenes, and applications. This Special Issue emerges from the above conference and includes eight articles that attempt to use geospatial big data to facilitate and promote the UPUM.

Human behaviors interact with service facilities, public transportation, open places in both the virtual space and physical spaces, but quantified models are very difficult to develop for these interactions. The article Social Media as passive geo-participation in transportation planning – how effective are topic modelling & sentiment analysis in comparison with citizen surveys? by Lock and Pettit (2020) investigated the opportunities in using social media data (Tweets) to engage with citizens and customers about public transport performance, where a wide array of topics, sentiments, and relationships were extracted from social media data about the public transportation system in Sydney, Australia. The article Equity issues and the PeCUS index: An indirect analysis of community severance, by Lara and Rodrigues da Silva (2020), proposed an indicator, PeCUS, based on the OpenStreetMap data and human-involved scoring on a series of criteria, to assess the quality of Pedestrian Crossings and also an indirect assessment of community severance. The article Analysis of shopping behavior characteristics in the Keihanshin metropolitan area in Japan based on a person trip survey, by Yamada and Hayashida (2020), using personal survey data, revealed the characteristics of shopping behaviors on weekdays in the metropolitan area. These three articles exhibited the applicable examples of utilizing geospatial big data for quantified fine evaluation of the human-city interactions.

Urban sensing plays a key role in UPUM by providing multi-perspective fundamental information about the urban. In the physical space, the article Quantification of the openness of urban external space through urban section, by Tong et al. (2020), proposed an elaborate model to quantify the openness of spaces. The buildings and boundary data in Nanjing, China, were used to test their model with many urban external space data extracted to support this study. The article Characterizing the spatial and temporal variation of the land surface temperature hotspots in Wuhan from a local scale, by Yang et al. (2020), presented a latent pattern and morphology-based framework to characterize the spatial and temporal variations of Land Surface Temperature (LST) hotspots at local scale. The MODIS synthesized LST products are applied to generate the local scale LST pattern in Wuhan, China, from 2002 to 2017. Both articles addressed the urban micro-environment that is critical to urban planning.

Many factors affect the formation of urban population mobility. The article An investigation of the visual features of urban street vitality using a convolutional neural network, by Qi et al. (2020), explores which and how visual features are related to the urban street vitality by generating heat maps. The article Cluster and characteristic analysis of Shanghai metro stations based on metro card and land-use data, by Shen et al. (2020), classified metro stations by the pattern changing of passenger flow extracted from check-in data of metro cards in Shanghai. Moreover, it is found that the pattern changing of passenger flow is closely related to the urban land use. These two articles tell us that geospatial big data can help people for better understanding of factors and their effects on urban processes.

Geospatial big data could contribute to optimize urban planning. The article Using multi-agent simulation to predict natural crossing points for pedestrians and choose locations for mid-block crosswalks, by Smirnov, Dunaenko, and Kudinov (2020), proposed a multi-agent simulation approach to predict routes for road crossing and optimal location for crosswalk. The pairs of simulations before and after arranging additional crosswalk, as well as field study, proved that the proposed approach could provide necessary crosswalk to help reduce wrong crossing of the road.

Geospatial big data has demonstrated promising advantages among various disciplines. One of the central issues therein is to explore the potentials for the complicated issues in UPUM. We believe that these articles in the special issue have made some successful contributions on Geospatial Big Data for UPUM and will foster more significant work in the future.

Disclosure statement

No potential conflict of interest was reported by the authors.