Incorporating spatial non-stationarity to improve dasymetric mapping of population

Cockx, Kasper; Canters, Frank

doi:10.1016/j.apgeog.2015.07.002

Cited by 34 publications

(25 citation statements)

References 46 publications

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“…At high population density, an underestimation of the original census data was observed, whereas significant overestimation was observed at extremely low population density, especially for WorldPop. These types of errors were also observed in previous dasymetric modeling studies [44][45][46]. However, POI-EAHSI showed significantly higher accuracy than WorldPop in both tails of population density, especially for the low tail (R 2 = 0.57 vs. R 2 = 0.15).…”

Section: Accuracy Assessmentsupporting

confidence: 81%

Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data

Yang

Zhao

et al. 2019

Remote Sensing

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Fine-resolution population distribution mapping is necessary for many purposes, which cannot be met by aggregated census data due to privacy. Many approaches utilize ancillary data that are related to population density, such as nighttime light imagery and land use, to redistribute the population from census to finer-scale units. However, most of the ancillary data used in the previous studies of population modeling are environmental data, which can only provide a limited capacity to aid population redistribution. Social sensing data with geographic information, such as point-of-interest (POI), are emerging as a new type of ancillary data for urban studies. This study, as a nascent attempt, combined POI and multisensor remote sensing data into new ancillary data to aid population redistribution from census to grid cells at a resolution of 250 m in Zhejiang, China. The accuracy of the results was assessed by comparing them with WorldPop. Results showed that our approach redistributed the population with fewer errors than WorldPop, especially at the extremes of population density. The approach developed in this study—incorporating POI with multisensor remotely sensed data in redistributing the population onto finer-scale spatial units—possessed considerable potential in the era of big data, where a substantial volume of social sensing data is increasingly being collected and becoming available.

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Section: Accuracy Assessmentsupporting

confidence: 81%

Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data

Yang

Zhao

et al. 2019

Remote Sensing

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“…GWR is a local spatial statistical method for evaluating how the relationships between a dependent variable and one or more explanatory variables change spatially. As one of the useful tools to explore the spatial local heterogeneity, GWR has been widely used in many fields in recent years, For example, the geographic variation and impact factors of urban public green space availability [46], peri-urban agriculture [47], noise pollution [48], population [49] and resident recreation demand [50] have been investigated with GWR. The GWR method was usually compared with global spatial statistical methods, such as the ordinary least squares (OLS) regression, regression kriging, or co-kriging and the comparisons showed the advantages of GWR in improving mapping quality and exploring spatially varying local relationships [47][48][49][50][51].…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Effects of Main Impact Factors on Residential Land Price in Major Cities of China

Yang

et al. 2017

Sustainability

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Abstract:With the rapid development of land marketization in China, the spatial patterns of residential land prices in different regions have become increasingly complicated. The very high and continuously rising residential land prices in many cities are causing significant challenges to economic development and social stability. Yet, there has only been a limited amount of attempts made to model and analyze the regional dynamic changes of residential land price systematically, especially in term of the spatially varying effects of key demographic and economic factors. In this study we provided a perspective analysis of the changes of residential land prices in 2008, 2011 and 2014 based on the land price monitoring records of 105 cities and then conducted a geographically weighted regression (GWR) analysis on the relationships between residential land price and three major impact factors (i.e., immigrant population, gross domestic product (GDP) and investment in residential buildings). Results show that the areas in which GDP had relatively strong positive impacts on residential land price expanded with time. The negative effects of immigrant population on residential land price were mainly concentrated in the cities around the Bohai Rim and the area with negative effects gradually shrank in the three studied years. Conversely, the areas with negative correlation between investment in residential buildings and residential land price gradually expanded in size over time. A geographical detector was used to examine the relative importance of factors to residential land price. It was found that the GDP had more significant influence on residential land price than other factors and the influence of the three factors to overall variation in residential land price increased over the three studied years. These results underscore the importance of taking spatially varying effects of major driving factors into account in policy-making on regional land market.

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“…The global regression assumes that the relationships between the variables are homogeneous across space. However, spatial dependences often are not homogeneous across large geographical regions [55]. We were more concerned about areas where spatial redistribution is sensitive to climate change.…”

Section: Methodsmentioning

confidence: 99%

The Influence of Environmental Change (Crops and Water) on Population Redistribution in Mexico and Ethiopia

et al. 2019

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This paper discusses the effects of long-term environmental change (represented by the abundance or scarcity relative to the long-term average level of crop yield/river flow) and short-term environmental shock (represented by the maximum number of consecutive years below the median crop yield/river flow per decade) on population redistribution in Mexico and Ethiopia. Crop production and water resources, which are affected by climate change and influence human survival and activities, were selected as research variables. Two developing countries, namely, Mexico and Ethiopia, were selected as comparison cases. The results showed that short-term environmental shocks had no correlation with population redistribution. Short-term environmental shocks might fail to influence migration decisions or cause only temporary displacements that cannot be detected by demographic statistics. Among the long-term environmental change factors, only crop yield deviation was found to have a significant positive correlation with population redistribution. Based on two different datasets and two different decades, crop yield deviation is positively correlated with population redistribution; the correlation coefficients between crop yield deviation and population redistribution were 0.134 to 0.162 in Mexico and 0.102 to 0.235 in Ethiopia. When urbanization was considered as the control variable, the correlation coefficient between crop yield deviation and population redistribution in Mexico dropped by half, while it was almost the same in Ethiopia. However, Ethiopia's population redistribution was more clearly influenced by the population itself. Crop yield deviation relative to water flow deviation meant changes in livelihoods. Population redistribution is a possible means of adapting to changes in livelihood. Mexico exhibited high resilience to changes in livelihoods caused by long-term environmental change, especially in its densely populated areas. In contrast, Ethiopia was characterized mainly by high population growth and low population migration. People in some areas of Ethiopia were forced to endure hardship of livelihood deterioration or to stay where they were due to the difficulty of obtaining sufficient resources to afford the cost of migration.

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Incorporating spatial non-stationarity to improve dasymetric mapping of population

Cited by 34 publications

References 46 publications

Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data

Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data

Spatiotemporal Effects of Main Impact Factors on Residential Land Price in Major Cities of China

The Influence of Environmental Change (Crops and Water) on Population Redistribution in Mexico and Ethiopia

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