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Urban nature—such as greenness and parks—can alleviate distress and provide space for safe recreation during the COVID-19 pandemic. However, nature is often less available in low-income populations and communities of colour—the same communities hardest hit by COVID-19. In analyses of two datasets, we quantified inequity in greenness and park proximity across all urbanized areas in the United States and linked greenness and park access to COVID-19 case rates for ZIP codes in 17 states. Areas with majority persons of colour had both higher case rates and less greenness. Furthermore, when controlling for sociodemographic variables, an increase of 0.1 in the Normalized Difference Vegetation Index was associated with a 4.1% decrease in COVID-19 incidence rates (95% confidence interval: 0.9–6.8%). Across the United States, block groups with lower income and majority persons of colour are less green and have fewer parks. Our results demonstrate that the communities most impacted by COVID-19 also have the least nature nearby. Given that urban nature is associated with both human health and biodiversity, these results have far-reaching implications both during and beyond the pandemic. Access to green space has been a critical, and contentious, issue for neighbourhood inequality and health outcomes. This Analysis looks at how the COVID-19 pandemic interacts with availability of nature for urban residents.

India is one of the world’s most flood-prone countries, with present-day risks likely to be exacerbated by climate change in the coming decades. The type of risk varies by location, with the lives, homes, and livelihoods of residents of India’s coastal megacities threatened by coastal floods and storm surges while village-dwellers residing in rural flood plains may additionally lose both crops and livestock. Schools and health facilities throughout the country are also at risk. This multi-scale study employs several datasets, from multiple domains, to generate high-resolution estimates of potential exposure to fluvial and coastal floods for (1) urban and rural populations, (2) health facilities, and (3) educational facilities. Our results, presented at the state level, suggest high exposure to fluvial flooding with about 184 million or more than 1:7 of India’s population at risk. This proportion is somewhat higher for rural dwellers (15.8%) compared with urban residents (14.2%). Urban residents, however, are much more likely to be affected by coastal floods, likely due to the high population densities of India’s coastal megacities. In total, around 19,218 (15%) of health and 34,519 (18%) of educational facilities are exposed to either coastal or fluvial flood risks. A spatially detailed, locally refined, comprehensive flood risk assessment such as this is critical to inform and target public policy and guide disaster risk reduction plans. By improving infrastructure, increasing awareness, and developing proactive, targeted, and inclusive flood plans, communities can build resilience.

In May 2020, the New York City (NYC) Mayor’s Office of Climate Resiliency (MOCR) began convening bi-weekly discussions, called the Rapid Research and Assessment (RRA) Series, between City staff and external experts in science, policy, design, engineering, communications, and planning. The goal was to rapidly develop authoritative, actionable information to help integrate resiliency into the City’s COVID response efforts. The situation in NYC is not uncommon. Extreme events often require government officials, practitioners, and citizens to call upon multiple forms of scientific and technical assistance from rapid data collection to expert elicitation, each spanning more or less involved engagement. We compare the RRA to similar rapid assessment efforts and reflect on the nature of the RRA and similar efforts to exchange and co-produce knowledge. The RRA took up topics on social cohesion, risk communication, resilient and healthy buildings, and engagement, in many cases strengthening confidence in what was already known but also refining the existing knowledge in ways that can be helpful as the pandemic unfolds. Researchers also learned from each other ways to be supportive of the City of New York and MOCR in the future. The RRA network will continue to deepen, continue to co-produce actionable climate knowledge, and continue to value organizational sensemaking as a usable climate service, particularly in highly uncertain times. Given the complex, rare, and, in many cases, unfamiliar context of COVID-19, we argue that organizational sensemaking is a usable climate service.

In this case study of Greater Saigon, two types of satellite data are used to estimate the rate of change in urban spatial expansion, both horizontally and volumetrically (horizontal and vertical components), and integrates them with socioeconomic data to examine the correlates and potential causes of both kinds of change. We employ new data products – the Global Human Settlement Layer (GHSL) derived largely from Landsat and a Dense Sampling Method (DSM) product based on QuikSCAT – in combination with data from the 1999 and 2009 censuses of Vietnam. Unlike past studies, we examine horizontal and volumetric changes in urban form and pay particular attention to the role of migration in locations experiencing those different types of change. We find these two types of urban change occur at different rates and in different localities, with the highest rates of horizontal change occurring to the north of administrative Saigon. In contrast, we find the highest rates of volumetric change in the areas north of the central districts but mostly within administrative Saigon. In-migration is strongly associated with horizontal change, whereas increases in population density appears to drive volumetric change, controlling for other factors. Positive volumetric change is associated with necessary amenities of modern urban living, often in high-rise buildings found in dense population centers like Saigon: the increasing presence in households of computers, air conditioners, piped water, and gas fuel. Use of these new integrated data hold promise to shed new light on both the built-environment and social dimensions of urbanization in low- and middle-income settings.

Diarrhea is a major contributor to child morbidity and mortality in West Africa. Non-spatial regression and geographically weighted Poisson regression applied to data from 10 Demographic an...

Over the last century, numbers of wild tigers (Panthera tigris) have crashed, while human populations have boomed. Here we investigate future trajectories of human population within tiger range through analysis of the shared socio-economic pathways (SSPs). These five pathways describe urban, rural and total population distributions by decade through 2100, based on plausible but contrasting scenarios of economic, education, migration, and urbanization policy. In 2010 approximately 57 million people lived in regions defined as “tiger conservation landscapes” (or TCLs); 8% of sympatric people lived in towns and cities that occupied 4% of tiger range. We show that tigers could share these same geographies with as few as 40 million (30% decline compared to 2010) or as many as 106 million people (an increase of 85%) by 2100. Those populations could be as much as 64%, or as little as 17%, urbanized, depending on the pathway. Urban areas are likely to expand, displacing between 6 and 22% of tiger's current range, depending on how urban growth is managed. Human population density thresholds compatible with tigers vary by region, from 140 persons/km2 in the Indian subcontinent, to 10 persons/km2 in the Russian Far East and northern China. SSP3, a future where nations indulge regional rivalries, would make conservation more difficult, whereas SSP1, with a focus on well-managed urbanization and education, could help relieve pressures. Tigers are a conservation-reliant species and will likely remain so through the 21st century, therefore we suggest coupling continued site-level protection with efforts to develop constituencies for conservation in Asia's burgeoning cities.

There is an increasing availability of geospatial data describing patterns of human settlement and population such as various global remote-sensing based built-up land layers, fine-grained census-based population estimates, and publicly available cadastral and building footprint data. This development constitutes new integrative modelling opportunities to characterize the continuum of urban, peri-urban, and rural settlements and populations. However, little research has been done regarding the agreement between such data products in measuring human presence which is measured by different proxy variables (i.e., presence of built-up structures derived from different remote sensors, census-derived population counts, or cadastral land parcels). In this work, we quantitatively evaluate and cross-compare the ability of such data to model the urban continuum, using a unique, integrated validation database of cadastral and building footprint data, U.S. census data, and three different versions of the Global Human Settlement Layer (GHSL) derived from remotely sensed data. We identify advantages and shortcomings of these data types across different geographic settings in the U.S., which will inform future data users on implications of data accuracy and suitability for a given application, even in data-poor regions of the world.

Urban nature can alleviate distress and provide space for safe recreation during the COVID-19 pandemic. However, nature is often less available in low-income and communities of color—the same communities hardest hit by COVID-19. We quantified nature inequality across all urbanized areas in the US and linked nature access to COVID-19 case rates for ZIP Codes in 17 states. Areas with majority persons of color had both higher case rates and less greenness. Furthermore, when controlling for socio-demographic variables, an increase of 0.1 in Normalized Difference Vegetation Index (NDVI) was associated with a 4.1% decrease in COVID-19 incidence rates (95% confidence interval: 0.9-6.8%). Across the US, block groups with lower-income and majority persons of color are less green and have fewer parks. Thus, communities most impacted by COVID-19 also have the least nature nearby. Given urban nature is associated with both human health and biodiversity, these results have far-reaching implications both during and beyond the pandemic.

Extreme heat has been responsible for more deaths in the United States than any other weather-related phenomenon over the past decade. The frequency and intensity of extreme heat events are projected to increase over the course of this century. In this work, we examine historical patterns of extreme heat exposure and mortality in the continental United States. We examine spatial variation in the mortality response to exposure, consider the contribution of key demographic and socio-economic factors in driving heat-related mortality, and test three different extreme heat thresholds using a national-level spatial autoregressive model and a geographically weighted regression approach. We find that the mortality response to exposure is higher in areas that do not routinely experience heat extremes, and that exposure itself is a stronger driver of heat-related mortality across the larger urban areas of the Midwest and Northeast. The importance of demographic/socio-economic factors varies substantially over space, and results are robust across alternative measures of heat extremes, suggesting that no single definition is necessarily superior. The baseline relationships established here are potentially useful for future predictions of exposure and heat-related mortality under alternative population and climate change scenarios, and may aid policy makers and planners in implementing effective adaptation and mitigation strategies.

The collection, processing and analysis of remote sensing data since the early 1970s has rapidly improved our understanding of change on the Earth’s surface. While satellite-based earth observation has proven to be of vast scientific value, these data are typically confined to recent decades of observation and often lack important thematic detail. Here, we advance in this arena by constructing new spatially-explicit settlement data for the United States that extend back to the early nineteenth century, and is consistently enumerated at fine spatial and temporal granularity (i.e., 250 m spatial, and 5 a temporal resolution). We create these time series using a large, novel building stock database to extract and map retrospective, fine-grained spatial distributions of built-up properties in the conterminous United States from 1810 to 2015. From our data extraction, we analyse and publish a series of gridded geospatial datasets that enable novel retrospective historical analysis of the built environment at unprecedented spatial and temporal resolution. The datasets are available at https://dataverse.harvard.edu/dataverse/hisdacus (Uhl and Leyk, 2020a, b, c, d).

Urban tree cover provides benefits to human health and well-being, but previous studies suggest that tree cover is often inequitably distributed. Here, we use National Agriculture Imagery Program digital ortho photographs to survey the tree cover inequality for Census blocks in US large urbanized areas, home to 167 million people across 5,723 municipalities and other Census-designated places. We compared tree cover to summer land surface temperature, as measured using Landsat imagery. In 92% of the urbanized areas surveyed, low-income blocks have less tree cover than high-income blocks. On average, low-income blocks have 15.2% less tree cover and are 1.5⁰C hotter than high-income blocks. The greatest difference between low- and high-income blocks was found in urbanized areas in the Northeast of the United States, where low-income blocks in some urbanized areas have 30% less tree cover and are 4.0⁰C hotter. Even after controlling for population density and built-up intensity, the positive association between income and tree cover is significant, as is the positive association between proportion non-Hispanic white and tree cover. We estimate, after controlling for population density, that low-income blocks have 62 million fewer trees than high-income blocks, equal to a compensatory value of $56 billion ($1,349/person). An investment in tree planting and natural regeneration of $17.6 billion would be needed to close the tree cover disparity, benefitting 42 million people in low-income blocks.

Sub-Saharan Africa is experiencing rapid urban growth. Cities enable greater access to health services and improved water and sanitation infrastructure, leading to some improvements in health. However, urban settings may also be associated with more sedentary, stressful lifestyles and consumption of less nutritious food. C-reactive protein (CRP) is a measure of chronic inflammation predictive of cardiovascular disease, and high body mass index (BMI), a ratio of weight to height, indicates overweight or obesity and is associated with an increased risk of many chronic diseases. To explore the association between urbanicity and these two markers, we overlaid data from the 2010 Tanzania Demographic and Health Survey (DHS) with a satellite-derived measure of built environment. Linear regression models were constructed for the outcomes of BMI and CRP, by 1) administratively defined urban/rural categorization from the DHS, 2) satellite derived built environment, and 3) built environment stratified by urban/rural. A total of 2,212 women were included; 23% had elevated CRP, 21% were overweight or obese. A third (33%) lived in a highly built up area and 29% lived in an area classified as urban. A strong positive association between both CRP and BMI and built environment was detected; log CRP increased 0.43 in the highest built up areas compared to not built up (p

While the population of the United States has been predominantly urban for nearly 100 years, periodic transformations of the concepts and measures that define urban places and population have taken place, complicating over-time comparisons. We compare and combine data series of officially-designated urban areas, 1990–2010, at the census block-level within Metropolitan Statistical Areas (MSAs) with a satellite-derived consistent series on built-up area from the Global Human Settlement Layer to create urban classes that characterize urban structure and provide estimates of land and population. We find considerable heterogeneity in urban form across MSAs, even among those of similar population size, indicating the inherent difficulties in urban definitions. Over time, we observe slightly declining population densities and increasing land and population in areas captured only by census definitions or low built-up densities, constrained by the geography of place. Nevertheless, deriving urban proxies from satellite-derived built-up areas is promising for future efforts to create spatio-temporally consistent measures for urban land to guide urban demographic change analysis.

Population data represent an essential component in studies focusing on human-nature interrelationships, disaster risk assessment and environmental health. Several recent efforts have produced global and continental-extent gridded population data which are becoming increasingly popular among various research communities. However, these data products, which are of very different characteristics and based on different modeling assumptions, have never been systematically reviewed and compared which may impede their appropriate use. This article fills this gap and presents, compares and discusses a set of large-scale (global and continental) gridded datasets representing population counts or densities. It focuses on data properties, methodological approaches and relative quality aspects that are important to fully understand the characteristics of the data with regard to the intended uses. Written by the data producers and members of the user community, through the lens of the “fitness for use” concept, the aim of this paper is to provide potential data users with the knowledge base needed to make informed decisions about the appropriateness of the data products available in relation to the target application and for critical analysis.

Given downward trends in fertility and mortality, population dynamics –and thus theestimation of spatially-explicit population dynamics and gridded population and derivativeproducts– are increasingly sensitive to mobility processes and their changes in spatiality. In thispaper, we present a procedure to produce origin-destination intermunicipal/intercounty andinterstate migration matrices, briefly discussing their use and application in gridded populationproducts. To illustrate our approach, we produce total and sex-specific matrices with informationfrom the 2000 and 2010 Mexican Census long-form 10% surveys. We share the code required toreproduce the extraction of these and for potentially at least another 122 country-periods based onharmonized publicly-available data from IPUMS International, which allow for the addition ofancillary social and economic data and individual and household levels, or IPUMS Terra, whichfurther allow for GIS-based mapping, visualization, and manipulation and for the merging ofimportant contextual, e.g., environmental, data. Besides discussing the likely limitations of thesemeasures, using official projections from the Mexican government, we illustrate howmigration/mobility data improve the estimation of spatial/gridded population dynamics. We wrapup with a call for the collection of more adequate, spatially-explicit data on residential mobility andmigration globally.

Most of future population growth will take place in the world’s cities and towns. Yet, there is no well-established, consistent way to measure either urban land or people. Even census-based urban concepts and measures undergo frequent revision, impeding rigorous comparisons over time and place. This study presents a new spatial approach to derive consistent urban proxies for the US. It compares census-designated urban blocks with proxies for land-based classifications of built-up areas derived from time-series of the Global Human Settlement Layer (GHSL) for 1990–2010. This comparison provides a new way to understand urban structure and its changes: Most land that is more than 50% built-up, and people living on such land, are officially classified as urban. However, 30% of the census-designated urban population and land is located in less built-up areas that can be characterized as mainly suburban and peri-urban in nature. Such insights are important starting points for a new urban research program: creating globally and temporally consistent proxies to guide modelling of urban change.

Urban areas are key sources of greenhouse gas (GHG) emissions and also are vulnerable to climate change. The recent IPCC Fifth Assessment Report illustrates a clear need for more research on urban strategies for climate change adaptation and mitigation. However, missing from the current literature on climate change and urban areas is a conceptual framework that integrates mitigation and adaptation perspectives and strategies. Because cities vary with respect to development histories, economic structure, urban form, institutional and financial capacities among other factors, it is critical to develop a framework that permits cross-city comparisons beyond simple single measures like population size. The primary purpose of this paper is to propose a conceptual framework for a multi-dimensional urbanization climate change typology that considers the underlying and proximate causes of GHG emissions and climate change vulnerabilities. The paper reviews some of the basic steps required to build such a typology and associated challenges that must be overcome via a demonstration of a pilot typology with nine case study cities. The paper shows how the proposed framework can be used to evaluate and compare the conditions of GHG emissions and climate change vulnerability across cities at different phases in the urbanization process.

Today’s demographic methods inherit traditions from an era when far less than half of the world’s population lived in cities and towns. Now that the world’s population as a whole has become more urban than rural, it is an opportune moment to ask whether these methods and the concepts that have informed them would benefit from a reappraisal. To ensure that demographic thinking continues to add value in the upcoming urban era, we should ask: What features of urban context and demographic behavior are distinctive from rural? In what way do the multiple spaces of urban life—its neighborhoods, the social networks that traverse neighborhoods, the connections and flows that reach across cities and link to rural areas, the multiple levels and units of government, and the highly diverse private sector of the urban economy—need to be considered in fashioning new methodological and conceptual tools? In the high-income West, one can draw insight from a venerable tradition of detailed sociological, geographic, and economic analysis of cities. But it remains surprising how limited to high-income settings this ‘‘Chicago School’’ of analysis has been, and how seldom its theoretical apparatus has been trained on the cities and towns of poor countries. Furthermore, to rely on methods that for the most part treat the wide variety of cities and towns as if they are all

Key Points Question What is the county-level prevalence of extreme obesity in the United States? Findings In this cross-sectional study of adults in the United States, county-level prevalence of self-reported extreme obesity ranged from 1.3% to 15.7%. Several prominent clusters of high prevalence were identified, including in the Mississippi Delta and the Southeast. Meaning Prevalence of extreme obesity appears to vary considerably by county; heterogeneity is obscured by available state-level prevalence estimates., Importance The prevalence of extreme obesity continues to increase among adults in the US, yet there is an absence of subnational estimates and geographic description of extreme obesity. This shortcoming prevents a thorough understanding of the geographic distribution of extreme obesity, which in turn limits the ability of public health agencies and policy makers to target areas with a known higher prevalence. Objectives To use small-area estimation to create county-level estimates of extreme obesity in the US and apply spatial methods to identify clusters of high and low prevalence. Design, Setting, and Participants A cross-sectional analysis was conducted using multilevel regression and poststratification with data from the 2012 Behavioral Risk Factor Surveillance System and the US Census Bureau to create prevalence estimates of county-level extreme obesity (body mass index ≥40 [calculated as weight in kilograms divided by height in meters squared]). Data were included on adults (aged ≥18 years) living in the contiguous US. Analysis was performed from June 4 to December 28, 2018. Main Outcomes and Measures Multilevel logistic regression models estimated the probability of extreme obesity based on individual-level and area-level characteristics. Census counts were multiplied by these probabilities and summed by county to create county-level prevalence estimates. Moran index values were calculated to assess spatial autocorrelation and identify spatial clusters of hot and cold spots. Estimates of moderate obesity were obtained for comparison. Results Overall, the weighted prevalence of extreme obesity was 4.0% (95% CI, 3.9%-4.1%) and the prevalence of moderate obesity was 23.7% (95% CI, 23.4%-23.9%). County-level prevalence of extreme obesity ranged from 1.3% (95% CI, 1.3%-1.3%) to 15.7% (95% CI, 15.3%-16.0%). The Pearson correlation coefficient comparing model-predicted estimates with direct estimates was 0.81 (P, This cross-sectional study estimates county-level rates of extreme obesity identifies clusters of high and low prevalence within the US.

In today&rsquo, s increasingly urban world, understanding the components of urban population growth is essential. While the demographic components of natural increase and migration have received the overwhelming share of attention to date, this paper addresses the effects of administrative reclassification on urban population growth as derived from census data, which remain largely unstudied. We adopt a spatial approach, using the finest resolution US census data available for three decennial census periods, to estimate the magnitude of reclassification and examine the spatial-temporal variation in reclassification effects. We supplement the census data by using satellite-derived settlement data to further explain reclassification outcomes. We find that while 10% and 7% of the population live in areas that underwent urban/rural reclassification during the 1990&ndash, 2000 and 2000&ndash, 2010 time periods, respectively (with smaller fractions of corresponding land), reclassification has a substantial effect on metrics derived to characterize the urbanization process&mdash, comprising roughly 44% and 34% of total urban population growth over each period. The estimated magnitude of this effect is sensitive to assumptions regarding the timing of reclassification. The approach also reveals where, how, to what degree, and, in some part, why reclassification is affecting to the process of urbanization on the fine spatial scale, including the impact of underlying demographic processes. This research provides new directions to more effectively study coupled nature&ndash, human systems and their interactions.

The original version of this article unfortunately contained a mistake. The name “Kluger” in the second paragraph should be changed to “Kugler”.

Global data on settlements, built-up land and population distributions are becoming increasingly available and represent important inputs to a better understanding of key demographic processes such as urbanization and interactions between human and natural systems over time. One persistent drawback that prevents user communities from effectively and objectively using these data products more broadly, is the absence of thorough and transparent validation studies. This study develops a validation framework for accuracy assessment of multi-temporal built-up land layers using integrated public parcel and building records as validation data. The framework is based on measures derived from confusion matrices and incorporates a sensitivity analysis for potential spatial offsets between validation and test data as well as tests for the effects of varying criteria of the abstract term built-up land on accuracy measures. Furthermore, the framework allows for accuracy assessments by strata of built-up density, which provides important insights on the relationship between classification accuracy and development intensity to better instruct and educate user communities on quality aspects that might be relevant to different purposes. We use data from the newly-released Global Human Settlement Layer (GHSL), for four epochs since 1975 and at fine spatial resolution (38m), in the United States for a demonstration of the framework. The results show very encouraging accuracy measures that vary across study areas, generally improve over time but show very distinct patterns across the rural-urban trajectories. Areas of higher development intensity are very accurately classified and highly reliable. Rural areas show low degrees of accuracy, which could be affected by misalignment between the reference data and the data under test in areas where built-up land is scattered and rare. However, a regression analysis, which examines how well GHSL can estimate built-up land using spatially aggregated analytical units, indicates that classification error is mainly of thematic nature. Thus, caution should be taken in using the data product in rural regions. The results can be useful in further improving classification procedures to create measures of the built environment. The validation framework can be extended to data-poor regions of the world using map data and Volunteered Geographic Information.

While malnutrition remains an important public health concern in poor countries, particularly among the rural and urban poor, overweight and obesity are emerging as important public health concerns for urban individuals. Globalization of the fast food industry and shifts in physical activity patterns in urban areas can result in different risks for overweight and obesity by socioeconomic status and sex for urban and rural residents. We examined Indian National Family Health Survey data, focusing on the association between urban residence and gains in body mass index among women (ages 15–49) in two survey rounds. We constructed multivariate models to assess the individual-level variation in BMI and the likelihood of becoming overweight or obese, finding that urban living is strongly associated with gains in BMI among women in India. Large cities have the highest proportions of overweight and obese women while spatial regression results suggest spatial clustering of higher BMIs in urbanized areas districts. Urban clustering of overweight and obesity may predict the future burden of chronic diseases for India but will benefit from further evaluation based on finer resolution data.

A B S T R A C T Urban growth is increasing the demand for freshwater resources, yet surprisingly the water sources of the world’s large cities have never been globally assessed, hampering efforts to assess the distribution and causes of urban water stress. We conducted the first global survey of the large cities’ water sources, and show that previous global hydrologic models that ignored urban water infrastructure significantly overestimated urban water stress. Large cities obtain 78 � 3% of their water from surface sources, some of which are far away: cumulatively, large cities moved 504 billion liters a day (184 km 3 yr � 1 ) a distance of 27,000 � 3800 km, and the upstream contributing area of urban water sources is 41% of the global land surface. Despite this infrastructure, one in four cities, containing $4.8 � 0.7 trillion in economic activity, remain water stressed due to geographical and financial limitations. The strategic management of these cities’ water sources is therefore important for the future of the global economy.

India is the world’s most populous country, yet also one of the least urban. It has long been known that India’s official estimates of urban percentages conflict with estimates derived from alternative conceptions of urbanization. To date, however, the detailed spatial and settlement boundary data needed to analyze and reconcile these differences have not been available. This paper presents gridded estimates of population at a resolution of 1 km along with two spatial renderings of urban areas—one based on the official tabulations of population and settlement types (i.e., statutory towns, outgrowths, and census towns) and the other on remotely-sensed measures of built-up land derived from the Global Human Settlement Layer. We also cross-classified the census data and the remotely-sensed data to construct a hybrid representation of the continuum of urban settlement. In their spatial detail, these materials go well beyond what has previously been available in the public domain, and thereby provide an empirical basis for comparison among competing conceptual models of urbanization.

This chapter assembles a quantitative portrait of the adolescent girls who migrate to the cities and towns of poor countries, drawing mainly on a large collection of data from demographic surveys and census micro-samples. For adolescent girls and young women, migration puts important urban resources within reach, in the form of access to higher levels of schooling, more varied labor markets and employment opportunities, and multiple levels of health-care institutions. But while the move is underway and until she locates a safe home in her new location, an adolescent girl can confront a range of social and sexual risks that can threaten her well-being and thwart hopes for advancement. Much of the literature on adolescent migration is focused on these risks but neglects the potential benefits. We find that in many countries, significant percentages of urban adolescent girls are recent in-migrants. In characterizing their life-circumstances, we give special attention to indicators of social isolation, the conditions of housing and neighborhood, and school enrollment. We show that adolescent girl migrants are a highly diverse group, advantaged in some respects and disadvantaged in others. Field studies in urban India shed light on the difficulties with which girls must cope as they strive to adapt to urban life.

GIScience 2016 Short Paper Proceedings Assessing Spatiotemporal Agreement between Multi- Temporal Built-up Land Layers and Integrated Cadastral and Building Data Johannes H. Uhl 1 , Stefan Leyk 1 , Aneta J. Florczyk 2 , Martino Pesaresi 2 , Deborah Balk 3 University of Colorado Boulder, Department of Geography, Boulder, CO 80309, U.S.A. Email: {johannes.uhl; stefan.leyk}@colorado.edu European Commission – Joint Research Centre (JRC), Institute for the Protection and Security of the Citizen (IPSC), Global Security and Crisis Management Unit, 21027 Ispra, Italy Email: {martino.pesaresi; aneta.florczyk}@jrc.ec.europa.eu City University of New York, Institute for Demographic Research and Baruch College, New York, NY 10010, U.S.A. Email: deborah.balk@baruch.cuny.edu Abstract There is an increasing availability of multi-temporal land use and built-up land datasets. However, little research has been done regarding the spatiotemporal uncertainty of these data products. In this work we present an approach that has the potential to be applicable for spatiotemporal evaluation of the novel Global Human Settlement Layer (GHSL) created by automatic classification of global collections of Landsat data recorded in the epochs 1975, 1990, 2000, and 2014. The proposed approach produces the reference data by integrating publicly available parcel and building data and derives agreement statistics with the GHSL. 1. Introduction The Global Human Settlement Layer (GHSL) project aims to assess the human presence in the planet by estimating the amount of built-up area based on remote sensing data and census data (Pesaresi et al. 2015). In Pesaresi et al. (2013) the GHSL information production workflow was tested for sensors ranging between 0.5 and 10m spatial resolution, which usually perform very well in detection of built-up areas but are typically constrained regarding data access, redistribution rights, and are typically not available consistently for long periods of time. For these reasons, these data are difficult to use for spatiotemporally uniform and systematic extraction of built-up areas on global, national or even regional level. Therefore, the GHSL workflow was tested with global collections of publicly available Landsat imagery collected in the past 40 years (Pesaresi et al. 2016). The Landsat GHSL dataset is available as seamless global mosaic at high spatial resolution (approx. 38m) and for various points in time (1975, 1990, 2000, 2014, see Figure 1a). GHSL data offers promising opportunities for population projections, disaster management and risk assessment (Freire et al. 2015; Freire et al. 2016), as well as for analysing and modelling urban dynamics and land use change. Before such novel data products can be made available to the research community, an extensive quality assessment is required. However, such assessments are difficult due to the lack of reliable reference data particularly for earlier time periods and in less developed regions. In this paper we present and discuss a novel approach to evaluate multi-temporal spatial data on built-up land such as GHSL or developed land cover classes using publicly available parcel (cadastral) data integrated with building footprints in the U.S. The aim of this study is to establish a protocol for future testing the accuracy of fine-scale multi-temporal products derived from automatic classification of satellite data featuring the presence of built-up areas. The selection of the test areas discussed here are driven and constrained by the availability of the reference data and consequently the results derived are