Population and Environment最新文献

Population densities provide valuable spatial information to identify populations at risk, quantify mobility, and improve our understanding of future urban settlements. Advancements in machine learning algorithms open up new horizons to face these challenges. This research proposes a supervised machine learning approach, Random Forest, for population density appraisal in a large and dense developing city. We studied Bogotá, where functional integration with neighboring municipalities exists, although they have different governments and uncoordinated urban development plans. As a starting point, we use simulated residential land-use patterns, classified according to socioeconomic levels, from a cellular automata-based model. We estimate population density with reliable land-use change models and nine simple representations of the urban structure, such as land values and the distance to urban amenities. Therefore, combining a cellular automata model with a classification model, considering both continuous and categorical variables, demonstrates this methodology’s potential and promises a reliable assessment of population density. Finally, we present a trip generation model integrated with densities and spatial location. A comprehensive results discussion suggests this study’s importance in urban planning and the accuracy of the proposed methodology to support decision-making processes and policy evaluation.

The study examines the relationship between sudden- and gradual-onset climate events and migration, hypothesizing that this relationship is mediated by the adaptive capacity of affected individuals. We use survey data from regions of Cambodia, Nicaragua, Peru, Uganda, and Vietnam that were affected by both types of events with representative samples of non-migrant residents and referral samples of migrants. Although some patterns are country-specific, the general findings indicate that less educated and lower-income people are less likely to migrate after exposure to sudden-onset climate events compared to their counterparts with higher levels of education and economic resources. These results caution against sweeping predictions that future climate-related events will be accompanied by widespread migration.

Universal access to safe drinking water is essential to population health and well-being, as recognized in the Sustainable Development Goals (SDG). To develop targeted policies which improve urban access to improved water and ensure equity, there is the need to understand the spatial heterogeneity in drinking water sources and the factors underlying these patterns. Using the Shannon Entropy Index and the Index of Concentration at the Extremes at the enumeration area level, we analyzed census data to examine the spatial heterogeneity in drinking water sources and neighborhood income in the Greater Accra Metropolitan Area (GAMA), the largest urban agglomeration in Ghana. GAMA has been a laboratory for studying urban growth, economic security, and other concomitant socio-environmental and demographic issues in the recent past. The current study adds to this literature by telling a different story about the spatial heterogeneity of GAMA's water landscape at the enumeration area level. The findings of the study reveal considerable geographical heterogeneity and inequality in drinking water sources not evidenced in previous studies. We conclude that heterogeneity is neither good nor bad in GAMA judging by the dominance of both piped water sources and sachet water (machine-sealed 500-ml plastic bag of drinking water). The lessons from this study can be used to inform the planning of appropriate localized solutions targeted at providing piped water sources in neighborhoods lacking these services and to monitor progress in achieving universal access to improved drinking water as recognized in the SDG 6 and improving population health and well-being.

What is the effect of migration on fuel use in rural Zambia? Opportunities to increase income can be scarce in this setting; in response, households may pursue a migration strategy to increase resources as well as to mitigate risk. Migrant remittances may make it possible for households to shift from primary reliance on firewood to charcoal, and the loss of productive labor through migration may reinforce this shift. This paper uses four waves of panel data collected as part of the Child Grant Programme in rural Zambia to examine the connection between migration and the choice of firewood or charcoal as cooking fuel and finds evidence for both mechanisms. Importantly, this paper considers migration as a process, including out- as well as return migration, embedding it in the context of household dynamics generally. Empirical results suggest that while migration helps move households away from firewood as a fuel source, return migration moves them back, but because firewood is more common, the overall effect of migration is to shift households away from primary reliance on firewood towards charcoal.