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基于ArcGIS的泰州市房价空间分析

时间:2021-04-27 20:57来源:毕业论文
人们对于这些地区楼盘的综合评价就会很高,房价也自然而然上去了,根据泰州市房价的空间分布状况利用各种分析方法从而得出影响房价的区位因素有:交通条件、基础设施、教育、

摘要: 在人们普遍关注房价的大背景下,本文的主要通过ArcGIS对泰州市房价进行分析,本文的数据主要来自泰州市搜房网,其中包括泰州市114个楼盘的销售平均价格、楼盘的地理坐标位置、泰州市各教育机构的地理坐标、医疗机构的地理坐标等等。运用空间自相关、克里金插值、缓冲区分析、叠加分析等分析方法,来综合分析泰州市房价的空间分布及影响它的区位因素。根据以上的研究得出结论是:泰州市楼盘的以城市聚集性为特点,主要分布在泰州城区、兴化市区、泰兴市区、姜堰市区,这些地方的房价要普遍高于周围其他地区,运用ArcGIS的缓冲区分析、叠加分析方法,分析其原因,主要是因为这些地区的基础设施完善,离教育机构、医疗机构比较近,对于居民来讲很方便,因而人们对于这些地区楼盘的综合评价就会很高,房价也自然而然上去了,根据泰州市房价的空间分布状况利用各种分析方法从而得出影响房价的区位因素有:交通条件、基础设施、教育、医疗。66534

毕业论文关键词:ArcGIS,泰州市房价,空间分析

Abstract:In widespread attention under the background of house prices, this article mainly through ArcGIS analysis of taizhou prices, in this paper, the data mainly from http://www.taizhousoufun。com/, including 114 building dish the average sales price, taizhou complex geographic coordinates, geographical coordinates of taizhou education institutions, medical institutions geographic coordinates, and so on. Using the spatial autocorrelation, kriging interpolation, analysis methods, such as buffer analysis, overlay analysis, comprehensive analysis to the spatial distribution and influence of taizhou prices its location factors. According to the above research conclusion is: taizhou city clustering for characteristics of buildings, mainly in taizhou city, XingHua town, taixing city, jiangyan city, where prices are higher than other surrounding areas, using ArcGIS buffer analysis, overlay analysis method, analyzes its reason, mainly because of the region's infrastructure is perfect, near education institutions, medical institutions, it is convenient for residents, so people for building the comprehensive evaluation of these areas will be very high, house prices are naturally go up, according to the spatial distribution of taizhou prices using various analytical method to draw the location factors that affect the housing prices are: transportation, infrastructure, education and medical care.

Keywords: ArcGIS, House prices of TaiZhou, Spacial Analysis 

目 录

1前  言 3

2 研究区域概况 3

3 数据及数据预处理 4

4 研究方法 8

4.1 空间自相关分析法 8

4.2 克里金插值法 9

5 泰州市房价的区位因素空间分析 9

5.1 全局空间自相关 9

5.2 局部空间自相关 10

5.3泰州房价的空间插值模拟 11

6 泰州市房价的因子分析 13

6.1 交通条件 13

6.2 基础设施条件 14

6.3 文化教育条件 14

6.4 医疗卫生条件 16

结语 19

参 考 文 献 20 基于ArcGIS的泰州市房价空间分析:http://www.751com.cn/shuxue/lunwen_74484.html

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