Human mobility is increasingly associated with environmental and climatic factors. One way to explore how mobility and the environment are linked is to review the research on different aspects of the topic. However, so many relevant articles are published that analysis of the literature using conventional techniques is becoming prohibitively arduous. To overcome this constraint, we have applied automated textual analysis. Using unsupervised topic modelling on 3197 peer-reviewed articles on the nexus between mobility and the environment published over the last 30 years, we identify 37 major topics. Based on their language use, the topics were deeply branched into two categories of focus: Impact and Adaptation. The Impact theme is further clustered into sub-themes on vulnerability and residential mobility, while articles within the Adaptation theme are clustered into governance, disaster management and farming. The analysis revealed opportunities for greater collaboration within environmental mobility research, particularly improved integration of adaptation and impact research. The topic analysis also revealed that, in the last 30 years, very little research appears to have been undertaken in migration destinations or on the fate of environmentally influenced migrants during their migration process and after arriving in a new location. There are also research gaps in gender and Indigenous issues within the Impact theme, as well as on adaptive capacity and capacity-building.

We conducted a systematic literature review of peer-reviewed full text articles on the nexus between human mobility and drought or heat published between 2001 and 2021, inclusive. We identified 387 relevant articles, all of which were analysed descriptively using a dictionary-based approach and by using an unsupervised machine learning–based Latent Dirichlet Allocation (LDA) model. Most articles were in response to droughts (71%), but heat and extreme temperature became more prominent after 2015. The drought-related literature focuses geographically on African and Southern Asian countries, while heat-related research has mainly been conducted in developed countries (mostly in the USA and Australia). For both hazards, European countries are under-represented. The LDA model identified 46 topics which were clustered into five major themes. One cluster (14% of all articles) included literature on heat-related mobility, mostly data-driven models, including amenity migration. The other four clusters included literature on drought, primarily on farming societies and the agricultural sector with three of those clusters making up 63% of all articles, with the common overarching focus on climate migration and food security. One of the four drought clusters focused on social dysfunction in relation to droughts. A sentiment analysis showed articles focusing on voluntary mobility as part of adaptation to drought and heat were more positive than articles focusing on migration triggered by droughts and heat. Based on the topics and the article characterisation, we identified various research gaps, including migration in relation to urban droughts, heat in farming societies and in urban societies of developing countries, planned retreat from hot to cooler places, and the inability or barriers to doing so. More research is also needed to understand the compound effect of drought and heat, and the social and psychological processes that lead to a mobility decision.

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.