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基于空间信息技术的村庄分布和消解特征定量化分析方法 ——以陕西省陕北地区为例
于 洋1, 王睿坤2, 雷振东3
1.西安建筑科技大学建筑学院,西部绿色建 筑国家重点实验室,教授,博士生导师, 694969303@qq.com;2.西安建筑科技大学建筑学院,硕士研究生;3.西安建筑科技大学建筑学院,教授,博士生 导师
摘要:
基于空间信息技术,构建了一套从 宏观层面研究村庄空间分布动态演变过程和 消解特征的定量化分析方法:首先,获取研 究区域内所选典型年份下的多源数据,包括 村庄POI数据;道路河流矢量数据;研究区 域DEM数据;运用Erdas从获取的遥感数据 中进一步提取耕地、乡村聚落斑块数据。其 次,运用Erdas、ArcGIS、SPSS相关技术,分 析研究区域内村庄整体空间分布动态演变进 程及整体消解特征;并进一步分析研究区域 内地理特征、河流水系、公路网、耕地这四大 因素与村庄空间分布及消解的关系特征。以 陕西省陕北地区为例,获取2010年2015年的 相关数据,进行整体空间分布和消解特征研 究,并分析高程、坡度、地貌、道路与村庄空 间分布及消解的关系特征。研究结果显示:村 庄主要分布在低山丘陵区,中山区村庄消解量与消解率均最高;5~15°区间,村庄数量分布最多, 消解量以及消解率最高;一半以上的村庄分布于黄土梁状梁峁状丘陵区及黄土峁梁状丘陵区;村 庄分布具有明显的公路指向性,远离公路的区域村庄易发生消解。该方法的构建有助于实现村 庄空间分布以及村庄消解时空特征的精确定量化分析,为宏观层面的村庄规划布局以及国土空 间规划中农业空间的划定等提供相应的技术方法支持。
关键词:  村庄  空间动态分布  消解时空特征  定量化分析方法  空间信息技术
DOI:10.13791/j.cnki.hsfwest.20190615
分类号:
基金项目:陕西省创新能力支撑计划项目(2018TD-013); 国家自然科学基金面上项目(51378422)
Quantitative Analysis Method for the Distribution and Decrease Characteristics of Villages Basedon Spatial Information Technology: A Case Study of Northern Shaanxi Province
YU Yang,WANG Ruikun,LEI Zhendong
Abstract:
In 2017, China’s urbanization rate reached 58.52%. Over the past two decades, China’s urbanization rate has increased by an average of 1.2% percent annually. It is expected that by 2030, China’s urbanization rate will increase to about 70%. With the rapid advancement of urbanization, under the influence factors and policy such as urban-rural potential gap, agricultural modernization, returning farmland to forestry and grassland, migrating and merging villages, the vast number of villages are declining and decreasing from the macro and micro levels. The microcosmic decline refers to the continuous transfer of dominant population within villages to cities, the transformation of part of rural land to urban land, the continuous reduction of rural construction and living system, and the gradual transformation of traditional agricultural production to modern industry. Macroscopic reduction refers to the reduction of the total number, area and density of villages at different rates in certain areas. Due to the lack of scientific, accurate and quantitative research methods on the phenomenon of village reduction at the macro level, the reduction of villages has been neglected in guidance and management in urban planning system for a long time. Under the new development background, it is necessary to accurately understand the dynamic evolution process of village distribution and the factors affecting village distribution, identify the regions where village reduction occurs at the macro level and the status and characteristics of village reduction in each region, figure out the factors that affecting village reduction and to analyze the impact of village reduction, find out the rule of village reduction, and provide help for the prediction of village reduction. In practice, it provides certain technical and methodological support for the delimitation of agricultural space in national land space planning. It is of great significance to carry out the planning and construction of rural settlements scientifically from the macro level and to promote the overall development of urban and rural areas. Hence, based on spatial information technology, a set of quantitative analysis methods to study the dynamic evolution of village spatial distribution and the overall decrease features are constructed: Firstly, obtaining the multi-source data of selected typical years, including the village POI data; road and river vector data, DEM data, and cultivated land and rural settlement patches data which are further extracted from the acquired remote sensing data by using Erdas; Secondly, using Erdas, ArcGIS, and SPSS to analyze the process of dynamic evolution of spatial distribution and overall reduction characteristic of the villages (the ANN method in ArcGIS is used to study whether villages are clustered, random or dispersed in different years. KED method in ArcGIS is used to find out the specific location, shape and size scale of village clustering in different years. What’s more, calculating the village quantity variables and the village settlement area variables, density variables, quantity and area variable rates in the whole study area or in the administrative divisions to judge the reduction status of villages); then studying the relationship between the four factors including geographical features, river systems, highway networks, cultivated land and the villages spatial distribution as well as the decrease feature. By counting the number of villages, the area of village settlement and the ANN index of villages in different geographical characteristics zones, combined with KED map, the spatial distribution of villages in different zones was summarized. The amount and area variables, decrease rate, density variables of villages in different geographical regions are counted to obtain the corresponding reduction characteristics. Taking northern Shaanxi Province as an example, the related data in 2010 and 2015 were obtained, the overall spatial distribution and decrease characteristics were studied, and the relationship between elevation, slope, landform, road and village spatial distribution and decrease characteristics were analyzed. The results show that in 2010 and 2015, the ANN index of northern Shaanxi was 0.749 and 0.763 respectively. The spatial distribution of villages was clustered. In 2010, the number of villages in northern Shaanxi was 31 694, and the density of villages was 0.396/ km2. In 2015, the number of villages in northern Shaanxi was 31 577, and the density of villages was 0.395 km2. In the past five years, 117 villages disappeared, the decrease rate was 0.37%, and the density variable was 0.001/km2. By overlapping the KED map with the administrative divisions of northern Shaanxi, we can see that there are four peaks of village density in northern Shaanxi, which are located in Fugu in the northern part, Hengshan, Mizhi in the central part and Jingbian, Wuqi in the western part, Huanglong and Luochuan in the southern part, and the village density structure of villages has hardly changed in 2015 compared with 2010. What’s more, villages in northern Shaanxi are mainly distributed in 800-1 200 m zones, and the decrease amount and decrease rate are the highest in 1 200~1 500 m zone; in the range of 5~15°, the number of villages is the largest, and the decrease amount and decrease rate are the highest; more than half of villages are distributed in the Loess beam-shaped hilly zone; the distribution of villages shows obvious road orientation, and villages in the areas far from highway are prone to decrease.
Key words:  Village  Spatial Dynamic Distribution  Spatial-Temporal Decrease Characteristic  Quantitative Analysis Method  Spatial Information Technology