Population Spatialization and Synthesis with Open Data

Long, Ying; Shen, Zhenjiang

doi:10.1007/978-3-319-19342-7_6

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…Extract parcels based on the road network (Long & Shen, 2015), exclude road areas from residential land (Cui et al, 2020), zoning of the study area (Wu et al, 2021), classify buildings based on buffer analysis of roads (Dong et al, 2019), etc. Presence/absence of class # POI (Stevens et al, 2015), number of POIs (Liu & Xiao, 2021), the kernel density value of POI (Zhao, 2019;Zhao et al, 2020), the distance to POI (Chen et al, 2021;Stevens et al, 2015), the kernel density standard deviation of POI (Chen et al, 2021), etc.…”

Section: Roadmentioning

confidence: 99%

“…Zoning of the study area (Liu & Xiao, 2021), classification of the urban functional zone (Li et al, 2018;Long & Shen, 2015), exclude non-human living areas from residential land (Cui et al, 2020), etc. (Patel et al, 2015), presence/ absence of buildings (Palacios-Lopez et al, 2022;Schug et al, 2021), area of buildings (Lwin & Murayama, 2009), proportion of building area (Palacios-Lopez et al, 2022;Schug et al, 2021), number of buildings (Huang et al, 2021), building volume/area of building floors (Chen et al, 2021;Lwin & Murayama, 2009;Palacios-Lopez et al, 2022;Schug et al, 2021), etc.…”

Section: Mediummentioning

confidence: 99%

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From auxiliary data to research prospects, a review of gridded population mapping

Lei

Xie

Cheng

et al. 2023

Transactions in GIS

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Population has significant application value and scientific significance in resource use, public health, public transportation, disaster assessment, and environmental management. However, traditional census data can not show the population density difference within census units. Furthermore, census data are not uniform across countries, and reconciling these differences when using data from multiple countries require considerable effort. Finally, there are scale differences between census and geospatial data (e.g., land use/cover), making data analysis and needed research difficult. These challenges significantly limit the applications of census data. The advent of gridded population mapping (GPM) technology has overcome these challenges. GPM technology has developed rapidly in recent years. The research data and models are rich and diverse, and many achievements have been made. A systematic review of the current state of GPM research will help relevant researchers and data users. This article begins by summarizing the core elements of GPM research in four aspects: auxiliary data, models, accuracy, and products. It will then go on to four problems prevalent in GPM research that have direct or indirect effects on the accuracy of GPM. Finally, the article prospects GPM research from four different aspects based on the current state of research.

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Section: Roadmentioning

confidence: 99%

Section: Mediummentioning

confidence: 99%

From auxiliary data to research prospects, a review of gridded population mapping

Lei

Xie

Cheng

et al. 2023

Transactions in GIS

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“…Hence, the spatial heterogeneity of agents’ characteristics must be taken into account with a granularity that depends on both model objectives and available spatial data (Zhu and Ferreira Jr, 2014; Chapuis et al, 2018). In many studies, the lack of geographic specificity has been identified as a major problem (Ji and Wan, 2021; Long and Shen, 2015; Anderson et al, 2014; Su et al, 2010; Nejad et al, 2021; Zhou et al, 2022). Simulated populations are often only allocated in census enumeration areas.…”

Section: Introductionmentioning

confidence: 99%

Allocating synthetic population to a finer spatial scale: An integer quadratic programming formulation

Yameogo

Hankach

Vandanjon

et al. 2022

Environment and Planning B: Urban Analytics and City Science

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Agent-Based Models (ABMs) are being increasingly used to evaluate urban systems, urban policies and environmental impacts. One prerequisite for using the ABM framework consists of generating a synthetic population representative of the actual population, featuring the appropriate attributes with respect to model objectives. A precise spatial positioning of the synthetic population agents is often key to ensuring ABM modeling quality. This paper considers the problem of allocating synthetic population agents to a finer spatial scale. Such an allocation process is performed from a higher-level statistical area where a synthetic population can be generated, that is, a container statistical area (CSA), to several nested non-overlapping elementary statistical areas (ESAs), where only marginals are available. This allocation step relies not only on common attributes between CSA and ESA, but also on additional discriminatory attributes, that is, attributes of interest, estimated from external data sources. The case study examined herein is based on French census and fiscal data. Common attributes include eight socio-demographic variables, totaling 17 modalities. An additional attribute of interest, that is, income, has also been added. The allocation problem at hand is modeled as an integer quadratic programming problem. An exact algorithm is first applied to solve the problem; the applicability of this algorithm proves to be limited to small-size synthetic populations. A heuristic is proposed to handle the allocation of larger-size synthetic populations. Tests carried out on the case study show that this heuristic yields near-optimal solutions; it is also computationally efficient and may fulfill the needs of a majority of users.

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“…Many researches focused on delineating population distribution or urban structure with social media data. Long and Shen [28] spatialized population with online check-in, POIs (Points of Interest), and other open data. Gao et al [29] detected city community network with interaction and movement of cell phone users.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Spatial Accessibility to General Hospitals with Navigation and Social Media Location Data: A Case Study in Nanjing

Qian

Chen

et al. 2020

IJERPH

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Spatial accessibility to general hospitals is an important indicator of the convenience and ability of residents to obtain medical services. Therefore, developing a model for measuring accessibility to general hospitals by multiple transportation modes is necessary. In this study, considering that the increase in travel time will reduce the attractiveness of general hospitals, we used the Two-Step Floating Catchment Area with the Gaussian attenuation function, in which the supply was presented by capacity of hospitals (i.e., number of beds), and the demand was presented by population in each grid derived with social media data mapping real-time locations of active users. The Gaussian Two-Step Floating Catchment Area (Ga2SFCA) simulates the attenuation tendency of the general hospital service capabilities over transit time. To obtain a highly precise understanding of accessibility to hospitals, transit time on Baidu Maps’ navigation service was used as the impedance condition, and the study area was divided into 1 square kilometer grids as the basic unit of research. Taking Nanjing city as a case study, it is found that the accessibility distribution shape changes from a multi-centered circular pattern to a multi-peak distribution, as the time threshold increases. By comparing the accessibility among 11 districts varying from main urban area to suburbs, the accessibility to general hospitals in Nanjing is significantly regionally unbalanced in both travel modes. By calculating and mapping the Modal Accessibility Gap (MAG) of the two travel modes, different modes of transportation resulted in different general hospital accessibility distributions. Generally, private car is superior in access to general hospitals to public transit in most areas. In the central area, public traffic may not contribute to the access to medical services as much as we thought, rather it plays a role in areas far from hospitals along metro lines and bus routes.

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Population Spatialization and Synthesis with Open Data

Cited by 8 publications

References 20 publications

From auxiliary data to research prospects, a review of gridded population mapping

From auxiliary data to research prospects, a review of gridded population mapping

Allocating synthetic population to a finer spatial scale: An integer quadratic programming formulation

Evaluating Spatial Accessibility to General Hospitals with Navigation and Social Media Location Data: A Case Study in Nanjing

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