pyShore: A deep learning toolkit for shoreline structure mapping with high-resolution orthographic imagery and convolutional neural networks

The process of mapping shoreline structures (i.e., riprap, groins, breakwaters or bulkheads) is heavily reliant on in-situ field surveys and manual delineation using orthoimagery or aerial imagery. These processes are time and resource intensive, resulting in update times of longer than a decade for larger waterbodies. In this study, we explore the effectiveness of a deep learning approach to map shoreline armoring structures from remotely sensed high-resolution imagery. We focus on computationally efficient techniques which can be deployed in desktop environments similar to those used by human coders today, with the goal of providing a semi-automated technique which reduces the total amount of time required to delineate shoreline structures. We test a range of architectures using a dataset of over 10,000 observations of four classes of shoreline structure, finding that a ResNet18 based Pyramid Attention Network (PAN) architecture achieves 72% overall accuracy (60 cm resolution), with 80% and 94% prediction accuracy in breakwater and groins, respectively. This relatively lightweight implementation enabled a 1.5 kilometers of shoreline to be processed in 1.4 s (GPU) to 2.16 s (CPU) in simulated user environments. Finally, we present pyShore, an implementation of this deep learning algorithm made available for human coders to apply as a part of a semi-automated workflow

Shrinking Suburbs in a Time of Crisis

The Routledge Companion to the Suburbs provides one of the most comprehensive examinations available to date of the suburbs around the world. International in scope and interdisciplinary in nature, this volume will serve as the definitive reference for scholars and students of the suburbs. This volume brings together the leading scholars of the suburbs researching in different parts of the world to better understand how and why suburbs and their communities grow, decline, and regenerate. The volume sets out four goals: 1) to provide a synthesis and critical appraisal of the historical and current state of understanding about the development of suburbs in the world; 2) to provide a forum for a comprehensive examination into the conceptual, theoretical, spatial, and empirical discontents of suburbanization; 3) to engage in a scholarly conversation about the transformation of suburbs that is interdisciplinary in nature and bridges the divide between the Global North and the Global South; and 4) to reflect on the implications of the socioeconomic, cultural, and political transformations of the suburbs for policymakers and planners. The Routledge Companion to the Suburbs is composed of original, scholarly contributions from the leading scholars of the study of how and why suburbs grow, decline, and transform. Special attention is paid to the global nature of suburbanization and its regional variations, with a focus on comparative analysis of suburbs through regions across the world in the Global North and the Global South. Articulated in a common voice, the volume is integrated by the very nature of the concept of a suburb as the unit of analysis, offering multidisciplinary perspectives from the fields of economics, geography, planning, political science, sociology, and urban studies.https://scholarworks.wm.edu/asbookchapters/1069/thumbnail.jp

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\u27s 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

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

The Influence of Internal Migration on Exposure to Extreme Weather Events in Mexico

Between 2005 and 2010, 6.3 million migrants (approximately 6% of the population) moved domestically within Mexico. These shifts have potential implications for exposure to natural disasters. To examine this relationship, we use census microdata in conjunction with information on natural disaster events. The populations exposed to extreme weather events are first calculated based on observed disasters and demographic change between 2005 and 2010. This is compared to a hypothetical scenario with no migration between 2005 and 2010. The results presented in this research note demonstrate that while migration has slightly decreased overall exposure within Mexico, this influence is highly localized in select areas, with internal migration increasing exposure in key urban destinations. This highlights the need to better understand the interacting roles of household-scale migratory decision making and economic/urban growth policy in climate change mitigation, and provides guidance on geographic regions to target for more detailed analysis

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. (C) 2015 Elsevier Ltd. All rights reserved

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