Grid-city management currently attracts a wider audience globally. Socio-economic data is an essential part of grid-city management system. Social-economic data of an urban is characterized by discrete, time-varying, ...
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ISBN:
(纸本)9780819469113
Grid-city management currently attracts a wider audience globally. Socio-economic data is an essential part of grid-city management system. Social-economic data of an urban is characterized by discrete, time-varying, statistical, distributed and complicated. Most of data are with no exactly spatial location or from various statistical units. There is obvious gap while matching social-economic data with existing grid map of natural geographical elements emerges. It may cause many difficulties in data input, organization, processing and analysis while the grid system constructing and executing. The issue of how to allocate and integrate the huge social-economic data into each grid effectively is crucial for grid-city construction. In this paper, we discussed the characteristics of social-economic data in a grid-city systematically, thereafter a cell-based model for social-economic data representing and analyzing is presented in this paper. The kernel issues of the cell-based model establishment include cell size deter-mining, cell capabilities developing for multidimension representation and evaluation, and cell dynamic simulation functions designing. The cell-based model supplements the methods system of spatial data mining, and is also promising m application to the spatialization of statistical data obtained from other researches including environmental monitoring, hydrological and meteorological observation.
To fairly distribute limited irrigation water resources in arid regions, a water allocation priority evaluation method based on remote sensing data was proposed and integrated with an optimization model. First, the wa...
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To fairly distribute limited irrigation water resources in arid regions, a water allocation priority evaluation method based on remote sensing data was proposed and integrated with an optimization model. First, the water supply response unit was divided according to canal system conditions. Then, a spatialization method was used for generating spatial agricultural output value (income from planting industry) and grain yield (yield of food crops) with the help of NDVI and the potential yield of farmland. Third, the AHP-TOPSIS method was employed to calculate the water allocation priority based on the above information. Finally, the evaluation results were integrated with a nonlinear multiobjective model to optimally allocate agricultural land and water resources, considering the combined objective of minimum envy and proportional fairness. The method was applied to Hetao irrigation area, an arid agriculture-dominant region in Northwest China. After solving the model, optimization alternatives were obtained, which indicate that: (1) the spatial method of agricultural output value can improve the accuracy by around 16% compared with the traditional method, and the spatial method of grain yield also have good accuracy (MAPE = 14.66%);(2) the rank of water allocation priority can reflect more spatial information, and provide practical decision support for the distribution of water resources;(3) the envy index can better improve the efficiency of an allocation system compared to the Gini coefficient method.
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