Deep learning fusion of satellite and social information to estimate human migratory flows.

Human migratory decisions are driven by a wide range of factors, including economic and environmental conditions, conflict, and evolving social dynamics. These factors are reflected in disparate data sources, including household surveys, satellite imagery, and even news and social media. Here, we present a deep learning-based data fusion technique integrating satellite and census data to estimate migratory flows from Mexico to the United States. We leverage a three-stage approach, in which we (1) construct a matrix-based representation of socioeconomic information for each municipality in Mexico, (2) implement a convolutional neural network with both satellite imagery and the constructed socioeconomic matrix, and (3) use the output vectors of information to estimate migratory flows. We find that this approach outperforms alternatives by approximately 10% (r 2), suggesting multi-modal data fusion provides a valuable pathway forward for modeling migratory processes.

geoBoundaries: A global database of political administrative boundaries.

We present the geoBoundaries Global Administrative Database (geoBoundaries): an online, open license resource of the geographic boundaries of political administrative divisions (i.e., state, county). Contrasted to other resources geoBoundaries (1) provides detailed information on the legal open license for every boundary in the repository, and (2) focuses on provisioning highly precise boundary data to support accurate, replicable scientific inquiry. Further, all data is released in a structured form, allowing for the integration of geoBoundaries with large-scale computational workflows. Our database has records for every country around the world, with up to 5 levels of administrative hierarchy. The database is accessible at http://www.geoboundaries.org, and a static version is archived on the Harvard Dataverse.

Taking the health aid debate to the subnational level: the impact and allocation of foreign health aid in Malawi.

OBJECTIVE: Cross-national studies provide inconclusive results as to the effectiveness of foreign health aid. We highlight a novel application of using subnational data to evaluate aid impacts, using Malawi as a case study. DESIGN: We employ two rounds of nationally representative household surveys (2004/2005 and 2010/2011) and geo-referenced foreign aid data. We examine the determinants of Malawi's traditional authorities receiving aid according to health, environmental risk, socioeconomic and political factors. We use two approaches to estimate the impact of aid on reducing malaria prevalence and increasing healthcare quality: difference-in-difference models, which include traditional authority and month-of-interview fixed effects and control for individual and household level time-varying factors, and entropy balancing, where models balance on health-related and socioeconomic baseline characteristics. General health aid and four specific health aid sectors are examined. RESULTS: Traditional authorities with greater proportions of individuals living in urban areas, more health facilities and greater proportions of those in major ethnic groups were more likely to receive aid. Difference-in-difference models show health infrastructure and parasitic disease control aid reduced malaria prevalence by 1.20 (95% CI -0.36 to 2.76) and 2.20 (95% CI 0.43 to 3.96) percentage points, respectively, and increased the likelihood of individuals reporting healthcare as more than adequate by 12.1 (95% CI 1.51 to 22.68) and 14.0 (95% CI 0.11 to 28.11) percentage points. Entropy balancing shows similar results. CONCLUSIONS: Aid was targeted to areas with greater existing health infrastructure rather than areas most in need, but still effectively reduced malaria prevalence and enhanced self-reported healthcare quality.

Domestic and International Climate Migration from Rural Mexico.

Evidence is increasing that climate change and variability may influence human migration patterns. However, there is less agreement regarding the type of migration streams most strongly impacted. This study tests whether climate change more strongly impacted international compared to domestic migration from rural Mexico during 1986-99. We employ eight temperature and precipitation-based climate change indices linked to detailed migration histories obtained from the Mexican Migration Project. Results from multilevel discrete-time event-history models challenge the assumption that climate-related migration will be predominantly short distance and domestic, but instead show that climate change more strongly impacted international moves from rural Mexico. The stronger climate impact on international migration may be explained by the self-insurance function of international migration, the presence of strong migrant networks, and climate-related changes in wage difference. While a warming in temperature increased international outmigration, higher levels of precipitation declined the odds of an international move.

Amplification or suppression: Social networks and the climate change-migration association in rural Mexico.

Increasing rates of climate migration may be of economic and national concern to sending and destination countries. It has been argued that social networks - the ties connecting an origin and destination - may operate as "migration corridors" with the potential to strongly facilitate climate change-related migration. This study investigates whether social networks at the household and community levels amplify or suppress the impact of climate change on international migration from rural Mexico. A novel set of 15 climate change indices was generated based on daily temperature and precipitation data for 214 weather stations across Mexico. Employing geostatistical interpolation techniques, the climate change values were linked to 68 rural municipalities for which sociodemographic data and detailed migration histories were available from the Mexican Migration Project. Multi-level discrete-time event-history models were used to investigate the effect of climate change on international migration between 1986 and 1999. At the household level, the effect of social networks was approximated by comparing the first to the last move, assuming that through the first move a household establishes internal social capital. At the community level, the impact of social capital was explored through interactions with a measure of the proportion of adults with migration experience. The results show that rather than amplifying, social capital may suppress the sensitivity of migration to climate triggers, suggesting that social networks could facilitate climate change adaptation in place.

Climate Change as Migration Driver from Rural and Urban Mexico.

Studies investigating migration as a response to climate variability have largely focused on rural locations to the exclusion of urban areas. This lack of urban focus is unfortunate given the sheer numbers of urban residents and continuing high levels of urbanization. To begin filling this empirical gap, this study investigates climate change impacts on U.S.-bound migration from rural and urban Mexico, 1986-1999. We employ geostatistical interpolation methods to construct two climate change indices, capturing warm and wet spell duration, based on daily temperature and precipitation readings for 214 weather stations across Mexico. In combination with detailed migration histories obtained from the Mexican Migration Project, we model the influence of climate change on household-level migration from 68 rural and 49 urban municipalities. Results from multilevel event-history models reveal that a temperature warming and excessive precipitation significantly increased international migration during the study period. However, climate change impacts on international migration is only observed for rural areas. Interactions reveal a causal pathway in which temperature (but not precipitation) influences migration patterns through employment in the agricultural sector. As such, climate-related international migration may decline with continued urbanization and the resulting reductions in direct dependence of households on rural agriculture.

Undocumented migration in response to climate change.

In the face of climate change induced economic uncertainty, households may employ migration as an adaptation strategy to diversify their livelihood portfolio through remittances. However, it is unclear whether such climate migration will be documented or undocumented. In this study we combine detailed migration histories with daily temperature and precipitation information for 214 weather stations to investigate whether climate change more strongly impacts undocumented or documented migration from 68 rural Mexican municipalities to the U.S. during the years 1986-1999. We employ two measures of climate change, the warm spell duration index (WSDI) and the precipitation during extremely wet days (R99PTOT). Results from multi-level event-history models demonstrate that climate-related international migration from rural Mexico was predominantly undocumented. We conclude that programs to facilitate climate change adaptation in rural Mexico may be more effective in reducing undocumented border crossings than increased border fortification.

What Makes Green Cities Unique? Examining the Economic and Political Characteristics of the Grey-to-Green Continuum.

In the United States, urbanization processes have resulted in a large variety-or "continuum"-of urban landscapes. One entry point for understanding the variety of landscape characteristics associated with different forms of urbanization is through a characterization of vegetative (green) land covers. Green land covers-i.e., lawns, parks, forests-have been shown to have a variety of both positive and negative impacts on human and environmental outcomes-ranging from increasing property values, to mitigating urban heat islands, to increasing water use for outdoor watering purposes. While considerable research has examined the variation of vegetation distribution within cities and related social and economic drivers, we know very little about whether or how the economic characteristics and policy priorities of green cities differ from those of "grey" cities-those with little green land cover. To address this gap, this paper seeks to answer the question how do the economic characteristics and policy priorities of green and grey cities differ in the United States? To answer this question, MODIS data from 2001 to 2006 are used to characterize 373 US cities in terms of their vegetative greenness. Information from the International City/County Management Association's (ICMA) 2010 Local Government Sustainability Survey and 2009 Economic Development Survey are used to identify key governance strategies and policies that may differentiate green from grey cities. Two approaches for data analysis-ANOVA and decision tree analysis-are used to identify the most important characteristics for separating each category of city. The results indicate that grey cities tend to place a high priority on economic initiatives, while green cities place an emphasis on social justice, land conservation, and quality of life initiatives.