Improving the Accuracy of Fine-Grained Population Mapping Using Population-Sensitive POIs

Zhao, Yuncong; Li, Qiangzi; Zhang, Yuan; Du, Xin

doi:10.3390/rs11212502

Cited by 30 publications

(16 citation statements)

References 54 publications

(74 reference statements)

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“…The 250 m scale and below is suitable for research in villages and communities, 500 m for that in counties and cities, and 1000 m for that in large-scale regional studies involving cities and provinces. According to existing research, the number of samples exceeding 50,000 affects the stability of the regression model and the accuracy of the model [13,40]. Therefore, the 500m scale was selected for modelling.…”

Section: Data Preparation For Modellingmentioning

confidence: 99%

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Optimization of Modelling Population Density Estimation Based on Impervious Surfaces

Zang

Zhang

Chen

et al. 2021

Land

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Population data are key indicators of policymaking, public health, and land use in urban and ecological systems; however, traditional censuses are time-consuming, expensive, and laborious. This study proposes a method of modelling population density estimations based on remote sensing data in Hefei. Four models with impervious surface (IS), night light (NTL), and point of interest (POI) data as independent variables are constructed at the township scale, and the optimal model was applied to pixels to obtain a finer population density distribution. The results show that: (1) impervious surface (IS) data can be effectively extracted by the linear spectral mixture analysis (LSMA) method; (2) there is a high potential of the multi-variable model to estimate the population density, with an adjusted R2 of 0.832, and mean absolute error (MAE) of 0.420 from 10-fold cross validation recorded; (3) downscaling the predicted population density from the township scale to pixels using the multi-variable stepwise regression model achieves a more refined population density distribution. This study provides a promising method for the rapid and effective prediction of population data in interval years, and data support for urban planning and population management.

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Section: Data Preparation For Modellingmentioning

confidence: 99%

“…POI data have been shown to be closely related to the population's distribution [38,39]. Furthermore, the accessibility of fine-grained geographic factor data has been applied by many scholars in the process of population spatialization [7,38,40].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Modelling Population Density Estimation Based on Impervious Surfaces

Zang

Zhang

Chen

et al. 2021

Land

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“…Many types of geospatial big data have consequently been produced, such as mobile phone use locations and points-of-interest (POI: e.g. street names, social venues and their locations), which contain information that can be used to estimate population distributions (Guha-Sapir & Hoyois 2015;Jiang et al 2015;Hu et al 2016;Zhao et al 2019). However, in the aftermath of a major disaster, with probable severe disruption of mobile phone networks in the impacted areas, POI-based population distribution estimates are unlikely to be available for many days.…”

Section: Introductionmentioning

confidence: 99%

Use of GIS and dasymetric mapping for estimating tsunami-affected population to facilitate humanitarian relief logistics: a case study from Phuket, Thailand

et al. 2022

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The 2004 Indian Ocean tsunami led to improvements in Thailand’s early warning systems and evacuation procedures. However, there was no consideration of better aid delivery, which critically depends on estimates of the affected population. With the widespread use of geographical information systems (GIS), there has been renewed interest in spatial population estimation. This study has developed an application to determine the number of disaster-impacted people in a given district, by integrating GIS and population estimation algorithms, to facilitate humanitarian relief logistics. A multi-stage spatial interpolation is used for estimating the affected populations using ArcGIS software. We present a dasymetric mapping approach using a population-weighted technique coupled with remote sensing data. The results in each target area show the coordinates of each shelter location for evacuees, with the minimum and maximum numbers of people affected by the tsunami inundation. This innovative tool produces not only numerical solutions for decision makers, but also a variety of maps that improve visualisation of disaster severity across neighbourhoods. A case study in Patong, a town of Phuket, illustrates the application of this GIS-based approach. The outcomes can be used as key decision-making factors in planning and managing humanitarian relief logistics in the preparedness and response phases to improve performance with future tsunami occurrences, or with other types of flood disaster.

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“…Several approaches have been developed to produce fine-scale gridded population data in the past few decades, such as areal weighting [20], spatial interpolation [21][22][23], and dasymetric mapping [24][25][26][27][28][29][30][31][32]. Among them, dasymetric mapping technology [33], which uses fine-scale auxiliary variables and specific weighting schemes to re-allocate census counts to grid cells, is the most widely used and effective one [19].…”

Section: Introductionmentioning

confidence: 99%

Mapping Multi-Temporal Population Distribution in China from 1985 to 2010 Using Landsat Images via Deep Learning

Zhuang

Yan

et al. 2021

Remote Sensing

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Fine knowledge of the spatiotemporal distribution of the population is fundamental in a wide range of fields, including resource management, disaster response, public health, and urban planning. The United Nations’ Sustainable Development Goals also require the accurate and timely assessment of where people live to formulate, implement, and monitor sustainable development policies. However, due to the lack of appropriate auxiliary datasets and effective methodological frameworks, there are rarely continuous multi-temporal gridded population data over a long historical period to aid in our understanding of the spatiotemporal evolution of the population. In this study, we developed a framework integrating a ResNet-N deep learning architecture, considering neighborhood effects with a vast number of Landsat-5 images from Google Earth Engine for population mapping, to overcome both the data and methodology obstacles associated with rapid multi-temporal population mapping over a long historical period at a large scale. Using this proposed framework in China, we mapped fine-scale multi-temporal gridded population data (1 km × 1 km) of China for the 1985–2010 period with a 5-year interval. The produced multi-temporal population data were validated with available census data and achieved comparable performance. By analyzing the multi-temporal population grids, we revealed the spatiotemporal evolution of population distribution from 1985 to 2010 in China with the characteristic of concentration of the population in big cities and the contraction of small- and medium-sized cities. The framework proposed in this study demonstrates the feasibility of mapping multi-temporal gridded population distribution at a large scale over a long period in a timely and low-cost manner, which is particularly useful in low-income and data-poor areas.

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Improving the Accuracy of Fine-Grained Population Mapping Using Population-Sensitive POIs

Cited by 30 publications

References 54 publications

Optimization of Modelling Population Density Estimation Based on Impervious Surfaces

Optimization of Modelling Population Density Estimation Based on Impervious Surfaces

Use of GIS and dasymetric mapping for estimating tsunami-affected population to facilitate humanitarian relief logistics: a case study from Phuket, Thailand

Mapping Multi-Temporal Population Distribution in China from 1985 to 2010 Using Landsat Images via Deep Learning

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