predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning
predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning
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DOI：
10.1016/j.compenvurbsys.2019.03.005
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发表时间：
2019-07-01
影响因子：
6.8
通讯作者：
Haworth, James
中科院分区：
文献类型：
--
作者：
Ibrahim, Mohamed R.;Titheridge, Helena;Haworth, James
对于发展中国家的政策制定者和政府来说，确定城市内当前和未来的非正式区域仍然是一个关键问题。在城市中识别此类区域的划定过程需要大量的资源。虽然有各种研究基于卫星图像分类识别非正式定居点，依赖于有监督或无监督机器学习方法，但这些模型要么需要多个输入数据才能运行，要么需要在精度方面进一步发展。本文介绍了一种利用十字路口数据识别非正式住区的新方法。通过如此少的输入数据，我们试图为规划者和政策制定者提供一种实用的工具，帮助他们识别城市中的非正式区域。该模型的算法基于空间统计和人工神经网络（ANN）。该模型基于非正式住区的两个普遍特征来定义非正式住区。我们将该模型应用于埃及和印度的五个主要城市，这些城市的空间结构中存在着非正式性。这些城市是埃及的大开罗、亚历山大、赫尔格达和明亚，以及印度的孟买。predict贫民窟模型在识别和预测同一城市或相似背景下不同城市的非正式性方面显示出很高的有效性和准确性。为了使该模型成为未来预测或进一步研究非正式住区和贫民窟的实用工具，我们为五个研究城市的预训练人工神经网络模型编写了python代码。
Identifying current and future informal regions within cities remains a crucial issue for policymakers and governments in developing countries. The delineation process of identifying such regions in cities requires a lot of resources. While there are various studies that identify informal settlements based on satellite image classification, relying on both supervised or unsupervised machine learning approaches, these models either require multiple input data to function or need further development with regards to precision. In this paper, we introduce a novel method for identifying and informal settlements using street intersection data. With such minimal input data, we attempt to provide planners and policy-makers with a pragmatic tool that can aid in identifying informal zones in cities. The algorithm of the model is based on spatial statistics and Artificial Neural Networks (ANN). The proposed model relies on defining informal settlements based on two ubiquitous characteristics of informal settlements. We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present. These cities are Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. The predictSLUMS model shows high validity and accuracy for identifying and predicting informality within the same city the model was trained on or in different ones of a similar context. To enable this model to be used as a pragmatic tool for future prediction or further research concerning informal settlements and slums, we have made the python code for the pre-trained ANN models of the five studied cities.