Amiruzzaman Md, Curtis Andrew, Zhao Ye, Jamonnak Suphanut, Ye Xinyue
Kent State University, Kent, USA.
Case Western Reserve University, Cleveland, USA.
J Comput Soc Sci. 2021;4(2):813-837. doi: 10.1007/s42001-021-00107-x. Epub 2021 Mar 8.
The complex interrelationship between the built environment and social problems is often described but frequently lacks the data and analytical framework to explore the potential of such a relationship in different applications. We address this gap using a machine learning (ML) approach to study whether street-level built environment visuals can be used to classify locations with high-crime and lower-crime activities. For training the ML model, spatialized expert narratives are used to label different locations. Semantic categories (e.g., road, sky, greenery, etc.) are extracted from Google Street View (GSV) images of those locations through a deep learning image segmentation algorithm. From these, local visual representatives are generated and used to train the classification model. The model is applied to two cities in the U.S. to predict the locations as being linked to high crime. Results show our model can predict high- and lower-crime areas with high accuracies (above 98% and 95% in first and second test cities, accordingly).
建筑环境与社会问题之间复杂的相互关系经常被描述，但往往缺乏数据和分析框架来探索这种关系在不同应用中的潜力。我们采用机器学习（ML）方法来解决这一差距，研究街道层面的建筑环境视觉信息是否可用于对高犯罪率和低犯罪率活动的地点进行分类。为了训练ML模型，使用空间化的专家叙述对不同地点进行标注。通过深度学习图像分割算法从这些地点的谷歌街景（GSV）图像中提取语义类别（如道路、天空、绿化等）。从中生成局部视觉代表并用于训练分类模型。该模型应用于美国的两个城市，以预测与高犯罪率相关的地点。结果表明，我们的模型能够高精度地预测高犯罪率和低犯罪率地区（在第一个测试城市和第二个测试城市中，准确率分别高于98%和95%）。
