Abstract
Statistical analysis of land-use change plays an important role in sustainable land management and has received increasing attention from scholars and administrative departments. However, the statistical process involving spatial overlay analysis remains difficult and needs improvement to deal with mass land-use data. In this paper, we introduce a spatio-temporal flow network model to reveal the hidden relational information among spatio-temporal entities. Based on graph theory, the constant condition of saturated multi-commodity flow is derived. A new method based on a network partition technique of spatio-temporal flow network are proposed to optimize the transition statistical process. The effectiveness and efficiency of the proposed method is verified through experiments using land-use data in Hunan from 2009 to 2014. In the comparison among three different land-use change statistical methods, the proposed method exhibits remarkable superiority in efficiency.
Highlights
Land use change is a key component of the Earth changes being found in all of the continents and having impacts on the Earth resource and services used by humankind [1,2,3,4,5]
GIS cannot facilitate query or analysis computations for entities that are beyond the representation capabilities of its data models [23], and the optimization of transition statistical processes is strictly limited unless the data model reveals further connections between simple topological relations
It only performs a spatial overlay of a data size that is about 1/1558 of the size in the basic Transition statistical process (TSP) and about 1/157 of the size in the query-optimized TSP for a five-year statistics
Summary
Land use change is a key component of the Earth changes being found in all of the continents and having impacts on the Earth resource and services used by humankind [1,2,3,4,5]. Type transition statistics are an important kind of spatio-temporal statistics that provide databases for the analytic indices and dynamic models. It concerns the measurable areal relationship characterizing changes between features and answers queries such as “how much farmland has changed to residential land over a period”. TSP can be obtained through overlay analysis (Figure 1), the process requires substantial time and resources when handling massive practical land-use data [14]. We propose and illustrate a new graph-based TSP method based on spatio-temporal change. We propose and illustrate a new graph-based TSP method based on the graph analysis. A query-optimized method using practical land-use data
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