Abstract
Accurate grid size suitability evaluations are necessary to enhance the spatialization quality of gridded population distributions. This paper proposes an improved average local variance (ALV) method to express discrepancies in population density and was validated in Anhui Province, China. A dataset consisting of 14 spatial scales, from 100 m to 900 m, and 1000 m to 5000 m, was processed by both the proposed and traditional ALV methods. Line graphs of two sets of ALV values and grid sizes were comparatively analyzed to evaluate the grid size suitability. The ALV trends calculated by the proposed method encompassed more accurate and useful features compared to the traditional method. The case study results showed that the 200 m grid size accurately expresses the population distribution characteristics of Anhui Province. The standard deviation (SD) index was adopted to validate these results; the proposed ALV method was proven valuable both in theory and practice for assessing grid size suitability. The method may be further improved by determining the essential laws of ALV values based on grid characteristics, and by enhancing the adaptability to various locations.
Highlights
Population data spatialization is conducted to unearth implicit information from traditional statistical data and distribute it across the geo-grid [1], making it a fundamental precondition for integration among spatial datasets and for accurately simulating population spatial distribution laws [2,3]
Ye, J. et al [8] have found that grid size suitability varied from different kinds of data sources, using a mathematical statistics method based on population grid data and statistical data of Yiwu City, Zhejiang Province
If the grid size is considerably finer than the objects in the image, much more cells will be highly correlated with their neighbors and the value of local variance will be low
Summary
Population data spatialization is conducted to unearth implicit information from traditional statistical data and distribute it across the geo-grid [1], making it a fundamental precondition for integration among spatial datasets and for accurately simulating population spatial distribution laws [2,3]. The characteristics of population distribution patterns vary among different grid sizes [4]. The primary goal of spatialization is the selection of a suitable grid size [5] that reflects the desired population distribution characteristics. To this effect, grid size suitability must be accurately evaluated to improve spatialization quality. Many recent researchers have explored grid sizes of population spatial data based on specific regional features. Du G. et al [6], for example, proved that population distribution depends on scale by applying geo-statistics methods to assess the spatial auto-correlation and variability of Shenyang City. P. et al [9] used a spatial autocorrelation index to analyze the spatialization characteristics of population density in the Shiyang River Basin, showing that the range from 8000 m to 10,000 m comprised suitable grid sizes
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
