Research on Building Data Spatialization Based on Feature Partition
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摘要: 针对现有基于多因素的建筑空间分布格网化模型未考虑建筑物空间分布异质性的问题,提出基于特征分区的建筑物数据空间化模型。以四川省雅安市为例,利用影响建筑物空间分布的因子进行建筑物特征一致性分区,选取土地利用、高程、坡度、坡向、河流距离、道路距离、地形起伏度7类影响因子,基于分区结果分别研究建筑物空间分布与各影响因子之间的关系,分区构建基于多因素的建筑物数据空间化模型,生成雅安市300 m格网建筑物空间分布数据。研究结果表明,分区构建的建筑物空间分布格网化模型有效提高了建筑物空间分布数据的精确度与准确性。Abstract: Aiming at the problem that the existed models do not consider the heterogeneity of building in spatial distribution, a gridding method of building data based on feature partition is proposed in this paper. Taking Ya'an City, Sichuan Province, as an example, the factors affecting the spatial distribution of buildings are used to partition the consistency of building characteristics. Seven influencing factors including land use, elevation, slope, aspect, river distance, road distance and topographic relief are selected. Based on the zoning results, the relationship between the spatial distribution of buildings and various influencing factors is studied respectively. The spatial gridding model of buildings based on multi factors is constructed, and the spatial distribution data of 300 m grid buildings in Ya'an City are generated. The results show that the gridding model of building spatial distribution constructed by zoning effectively improves the precision and accuracy of building spatial distribution data.
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Key words:
- Building /
- Feature partition /
- Space distribution /
- Gridding /
- Ya'an
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表 1 影响因子子类分级
Table 1. Classification of influencing factors
土地利用 高程/m 坡度/(°) 坡向/(°) 河流距离/m 道路距离/m 地形起伏度/m 耕地 500~800 平原
(0~0.5)平缓坡
(−1)0~200 0~200 平原(<30) 森林 800~1 100 微斜坡(0.5~2) 向阳坡(135~225) 200~400 200~400 台地
(30~70)草地 1 100~1 400 缓斜坡(2~5) 向阳坡
(45~135,
225~315)400~600 400~600 丘陵
(70~200)灌木林 1 400~1 700 斜坡
(5~15)阴坡(0~45,
315~360)600~800 600~800 小起伏山地(200~500) 湿地 1 700~2 000 陡坡
(15~35)— 800~1 000 800~1 000 中起伏山地(500~1 000) 水体 2 000~2 400 峭坡
(35~55)— 1 000~1 200 1 000~1 200 大起伏山地(1 000~2 500) 不透水面 2 400~2 800 垂直壁
(55~90)— 1 200~1 400 1 200~1 400 极大起伏山地(>2 500) 裸地 2 800~3 200 — — 1 400~1 600 1 400~1 600 — 冰川 3 200~3 800 — — 1 600~1 800 1 600~1 800 — — 3 800~4 400 — — 1 800~2 000 1 800~2 000 — — >4 400 — — >2 000 >2 000 — 表 2 雅安市不同特征分区抽样统计的各类因子权重
Table 2. Weights of various factors in sampling statistics in different regions of Ya'an city
分区 区域划分 权重 土地利用 高程 坡度 坡向 河流距离 道路距离 地形起伏度 一区 建设用地 — — 0.263 0.108 — 0.300 0.329 非建设用地 0.244 — 0.225 0.164 0.064 0.083 0.220 二区 建设用地 — — 0.230 0.231 0.144 0.159 0.236 非建设用地 0.163 — 0.178 0.154 0.187 0.141 0.177 三区 建设用地 — 0.216 0.213 0.156 — 0.217 0.198 非建设用地 0.227 0.177 0.224 — — 0.183 0.189 四区 建设用地 — 0.089 0.195 0.185 0.169 0.148 0.214 非建设用地 0.236 0.183 0.221 — — 0.175 0.185 五区 建设用地 — 0.176 0.183 0.169 0.156 0.147 0.170 非建设用地 0.156 0.168 0.192 0.180 — 0.148 0.157 表 3 相对误差分级统计
Table 3. Statistics of relative error classification
分级 数目 比例/% 严重低估,<−50% 794 4.7 一般低估,[−50%,−20%) 602 3.6 较准确估计,[−20%,20%] 12 568 74.7 一般高估,(20%,50%] 1 228 7.3 严重高估,>50% 1 626 9.7 -
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