LEARNING GEOGRAPHICAL DISTRIBUTION OF VACANT HOUSES USING CLOSED MUNICIPAL DATA: A CASE STUDY OF WAKAYAMA CITY, JAPAN
LEARNING GEOGRAPHICAL DISTRIBUTION OF VACANT HOUSES USING CLOSED MUNICIPAL DATA: A CASE STUDY OF WAKAYAMA CITY, JAPAN
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DOI：
10.5194/isprs-annals-vi-4-w2-2020-1-2020
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
H. Baba;Y. Akiyama;T. Tokudomi;Y. Takahashi
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文献类型：
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作者：
H. Baba;Y. Akiyama;T. Tokudomi;Y. Takahashi
抽象的。空置房检测是一个迫切需要解决的问题。这也是一个适当的例子，以促进利用存储在市政当局的智能数据。本研究提出了利用封闭市政数据的空置住宅检测模型，并考虑加速利用公共数据来促进智慧城市。采用机器学习技术，这项研究确保了空置房屋检测的高预测能力。该模型使我们能够处理复杂的市政数据，包括非线性特征特性和大量的缺失数据。特别是，处理丢失的数据在封闭的市政数据的实际使用中是重要的，因为不是所有的数据都必须被吸收到建筑单元。因此，本分析中的模型显示，准确率和假阳性率分别为95.4%和3.7%，足以检测空置房屋。但真阳性率为77.0%。虽然在某种程度上，该比率并不低，但特征的选择和额外样本的进一步收集可以提高该比率。空置房屋的地理分布进一步使我们能够检查实际空置房屋数量与估计空置房屋数量之间的差异，超过80%的500米网格数据的误差在10以下，我们认为这为城市规划者提供了翔实的数据，以大致把握地理趋势。
Abstract. Vacant housing detection is an urgent problem that needs to be addressed. It is also a suitable example to promote utilisation of smart data that are stored in municipalities. This study proposes a vacant housing detection model that uses closed municipal data and considers accelerating the use of public data to promote smart cities. Employing a machine learning technique, this study ensures high predictive power for vacant housing detection. The model enables us to handle complex municipal data that include non-linear feature characteristics and substantial missing data. In particular, handling missing data is important in the practical use of closed municipal data because not all of the data are necessarily absorbed to a building unit. Consequently, the model in this analysis showed that the accuracy and false positive rate are 95.4 percent and 3.7 percent, respectively, which are high enough to detect vacant houses. However, the true positive rate is 77.0 percent. Although the rate is not low to some extent, selection of features and further collection of extra samples may improve the rate. Geographic distribution of vacant houses further enabled us to check the difference between the actual and estimated number of vacant houses, and more than 80 percent of 500-meter grid data are with below 10 errors, which we think, provides city planners with informative data to roughly grasp geographical tendencies.