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The application of machine learning to rural population migration research
Population, Space and Place ( IF 2.630 ) Pub Date : 2023-05-22 , DOI: 10.1002/psp.2664
Hunter S. Baggen, Fiona Shalley, Andrew Taylor, Kerstin K. Zander

Many rural areas experience population stagnation or decline from out-migration with corresponding economic downturns. This is the case for the Northern Territory in Australia, a vast and sparsely populated jurisdiction. Its government has long sought to encourage stronger population growth but its population is young and highly transient, leading to high staff turn-overs and challenges for industries and government to attract families and skilled workers. Place-based factors such as job opportunities, access to essential services or environmental amenities influence satisfaction and migration decisions. The aim of this study was to understand why people might stay or move away through analysing responses to two open-text questions on the best and worst aspect of living in the Northern Territory. Over 3500 valid responses were analysed using machine learning-based unsupervised topic modelling which uncovered latent clusters. Forty-four percent of positive aspects were clustered into lifestyle factors, while negative aspects clustered around high living costs and crime. Some aspects, such as the weather and distance to other places were discussed as both positive and negative aspects. Topics discussed by respondents could be directly related to their intention to leave the Northern Territory, and also to specific individual's demographic characteristics providing insights for policies focused on attracting and retaining population. The use of unsupervised text mining in population research is rare and this study verifies its use to deliver objective and nuanced results generated from a large qualitative data set.

中文翻译:

机器学习在农村人口迁移研究中的应用

许多农村地区因人口外流而出现人口停滞或下降,并伴随着相应的经济衰退。澳大利亚北领地就是这种情况,该地区幅员辽阔,人口稀少。其政府长期以来一直寻求鼓励更强劲的人口增长,但其人口年轻且流动性大,导致员工流动率高,给行业和政府吸引家庭和技术工人带来挑战。工作机会、获得基本服务或环境设施等基于地点的因素会影响满意度和移民决策。这项研究的目的是通过分析关于北领地生活最好和最坏方面的两个开放文本问题的回答,了解人们为什么会留下或搬走。使用基于机器学习的无监督主题建模分析了 3500 多个有效响应,发现了潜在的集群。百分之四十四的积极方面集中在生活方式因素上,而消极方面集中在高生活成本和犯罪方面。一些方面,例如天气和到其他地方的距离,被讨论为积极和消极的方面。受访者讨论的主题可能与他们离开北领地的意图直接相关,也可能与特定个人的人口特征相关,为专注于吸引和留住人口的政策提供见解。在人口研究中使用无监督文本挖掘的情况很少见,本研究验证了其用于提供从大型定性数据集生成的客观且细致入微的结果的用途。
更新日期:2023-05-22
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