Global Rural Urban Mapping Project Research Articles

Do Refugee Camps Offer a Refuge From Conflict? A Spatially Explicit Analysis of Conflict Incidence at 1,543 Refugee Camps Across Africa (1997–2020)

By the end of 2020, 20.7 million refugees worldwide were under the protection of the United Nations High Commissioner for Refugees (UNHCR). Despite the intended role of refugee camps as sanctuaries for people fleeing conflict and persecution, recent empirical research has shown that many refugees continue to experience conflict even after settling in camps. Measuring refugee exposure to conflict, especially recurrent conflict, is important for the design and evaluation of refugee settlement and asylum policies, refugee-host relationships, as well as refugee security and protection. However, existing research is either nationally aggregated or highly localized at a small number of refugee camps and does not consider changes in conflict incidence following refugee arrivals, leaving uncertainty around near-camp conflict dynamics across refugee hosting countries. To address these gaps, we measured conflict event proximity, frequency, and trends around refugee and non-refugee settlements in all refugee-hosting countries in Africa over a 24-year period (1997–2020). We used georeferenced data on 1,543 refugee camps from UNHCR and conflict event data from the Armed Conflict Location Event Database (ACLED), and compared localized conflict incidence at refugee camps to 4,003 non-refugee settlements from the Global Rural-Urban Mapping Project (GRUMP). Our results show that 52% of all refugee camps and 94% of urban refugee camps were within 10 km of at least one armed conflict event after camp establishment. Conversely, only 82% of urban settlements without refugee camps were within 10 km of a conflict event, suggesting that urban refugee camps are subject to nearby conflict at a disproportionately higher rate compared to both rural refugee camps and non-refugee settlements. We also find that conflict events moved an average of 11.2 km closer to refugee camps after camp establishment, indicating a general encroachment of conflict upon camps. Such persistent and widespread conflict challenges the security of camps and the protections afforded to refugees, and merits increased attention from host countries and humanitarian actors.

Large conservation opportunities exist in >90% of tropic-subtropic coastal habitats adjacent to cities

Large conservation opportunities exist in >90% of tropic-subtropic coastal habitats adjacent to cities

Testing Heaps\u2019 law for cities using administrative and gridded population data sets

Since 2008 the number of individuals living in urban areas has surpassed that of rural areas and in the next decades urbanisation is expected to further increase, especially in developing countries. A country’s urbanisation depends both on the distribution of city sizes, describing the fraction of cities with a given population (or area), and the overall number of cities in the country. Here we present empirical evidence suggesting the validity of Heaps’ law for cities: the expected number of cities in a country is only a function of the country’s total population (or built-up area) and the distribution of city sizes. This implies the absence of correlations in the spatial distribution of cities. We show that this result holds at the country scale using the official administrative definition of cities provided by the Geonames dataset, as well as at the local scale, for areas of 128 times 128 km2 in the United States, using a morphological definition of urban clusters obtained from the Global Rural-Urban Mapping Project (GRUMP) dataset. We also derive a general theoretical result applicable to all systems characterised by a Zipf distribution of group sizes, which describes the relationship between the expected number of groups (cities) and the total number of elements in all groups (population), providing further insights on the relationship between Zipf’s law and Heaps’ law for finite-size systems.

Mapping Urban Extent at Large Spatial Scales Using Machine Learning Methods with VIIRS Nighttime Light and MODIS Daytime NDVI Data

Urbanization poses significant challenges on sustainable development, disaster resilience, climate change mitigation, and environmental and resource management. Accurate urban extent datasets at large spatial scales are essential for researchers and policymakers to better understand urbanization dynamics and its socioeconomic drivers and impacts. While high-resolution urban extent data products - including the Global Human Settlements Layer (GHSL), the Global Man-Made Impervious Surface (GMIS), the Global Human Built-Up and Settlement Extent (HBASE), and the Global Urban Footprint (GUF) - have recently become available, intermediate-resolution urban extent data products including the 1 km SEDAC’s Global Rural-Urban Mapping Project (GRUMP), MODIS 1km, and MODIS 500 m still have many users and have been demonstrated in a recent study to be more appropriate in urbanization process analysis (around 500 m resolution) than those at higher resolutions (30 m). The objective of this study is to improve large-scale urban extent mapping at an intermediate resolution (500 m) using machine learning methods through combining the complementary nighttime Visible Infrared Imaging Radiometer Suite (VIIRS) and daytime Moderate Resolution Imaging Spectroradiometer (MODIS) data, taking the conterminous United States (CONUS) as the study area. The effectiveness of commonly-used machine learning methods, including random forest (RF), gradient boosting machine (GBM), neural network (NN), and their ensemble (ESB), has been explored. Our results show that these machine learning methods can achieve similar high accuracies across all accuracy metrics (>95% overall accuracy, >98% producer’s accuracy, and >92% user’s accuracy) with Kappa coefficients greater than 0.90, which have not been achieved in the existing data products or by previous studies; the ESB is not able to produce significantly better accuracies than individual machine learning methods; the total misclassifications generated by GBM are more than those generated by RF, NN, and ESB by 14%, 16%, and 11%, respectively, with NN having the least total misclassifications. This indicates that using these machine learning methods, especially NN and RF, with the combination of VIIRS nighttime light and MODIS daytime normalized difference vegetation index (NDVI) data, high accuracy intermediate-resolution urban extent data products at large spatial scales can be achieved. The methodology has the potential to be applied to annual continental-to-global scale urban extent mapping at intermediate resolutions.

Prioritization of Watersheds across Mali Using Remote Sensing Data and GIS Techniques for Agricultural Development Planning

Implementing agricultural water management programs over appropriate spatial extents can have positive effects on water access and erosion management. Lack of access to water for domestic and agricultural uses represents a major constraint on agricultural productivity and perpetuates poverty and hunger in sub-Saharan Africa (SSA). This lack of access is the result of erratic precipitation, poor water management, limited knowledge of hydrological systems, and inadequate investment in water infrastructure. Water management programs should be made by multi-disciplinary teams that consider the interrelationship between hydraulic and anthropogenic factors. This paper proposes a method to prioritize watersheds for water management and agricultural development across Mali (Western Africa) using remote sensing data and GIS tools. The method involves deriving a set of relevant thematic layers from satellite imagery. Satellite images from Landsat ETM+ were used to generate thematic layers such as land use/land cover. Slope and drainage density maps were derived from Shuttle RADAR Topography Mission (SRTM) Digital Elevation Model (DEM) at 90 m spatial resolution. Population grids were available from the Global rural-urban mapping project (GRUMP) database for the year 2000 and mean rainfall maps were extracted from Tropical rainfall measuring mission (TRMM) grids for each year between 1988 and 2014. Each thematic layer was divided into classes that were assigned a rank for agriculture and livelihoods development provided by experts in the relevant field (e.g., Soil scientist ranking the soil classes) and published literature on those themes. Zones of priority were delineated based on the combination of high scoring ranks from each thematic layer. Five categories of priority zones ranging from “very high” to “very low” were determined based on total score percentages. Field verification was then undertaken in selected categories to check the priority assigned to each class using a random sampling method. Watershed boundaries were prepared at 1000 ha scale and overlaid on the priority map to identify watersheds that were in a very high priority zone. The importance and efficiency of using remote sensing to prioritize watershed interventions across countries is critical due to the limited technical and financial resources available in sub-Saharan Africa (SSA).

Quantitative Assessment of Climate Change Vulnerability of Irrigation Demands in Mediterranean Europe

This paper presents an analysis of water resources management under climate change in Southern European River Basin Districts. The analysis is based on the Water Availability and Adaptation Policy Analysis (WAAPA) model, which focuses on the quantitative evaluation of maximum potential water withdrawal for different types of demands. The Water Availability and Adaptation Policy Analysis model performs the simulation of water resources systems at the monthly time scale and allows the estimation of the demand-reliability curve in every subbasin of the river network. Over sixty River Basin Districts of Southern Europe have been analyzed, taking basic information from publicly available databases: basin topology from the Hydro1K database, average runoff from the University of New Hampshire Global Runoff Data Centre (GRDC) composite runoff field, population from the Global Rural–urban Mapping Project (GRUMP) and irrigation area from the Global Map of Irrigated Area dataset. Streamflow monthly time series were obtained from the results of the ENSEMBLES project in four climate scenarios for time horizon 2070–2100. Climate change vulnerability of irrigation demands is estimated from changes in maximum potential water withdrawals for irrigation in current and future scenarios. Maximum potential water withdrawal for irrigation was computed as the largest value of irrigation demand that could be supplied with a given reliability requirement once the existing urban demand is adequately satisfied. The results show significant regional disparities in vulnerability to climate change in the irrigation sector across Europe. The greatest vulnerabilities have been obtained for Southwest Europe (Iberian Peninsula) and some basins in Italy and Greece.

A Top-Down Regional Assessment of Urban Greenhouse Gas Emissions in Europe

This paper provides an account of urban greenhouse gas (GHG) emissions from 40 countries in Europe and examines covariates of emissions levels. We use a "top-down" analysis of emissions as spatially reported in the Emission Dataset for Global Atmospheric Research supplemented by Carbon Monitoring for Action from 1153 European cities larger than 50000 population in 2000 (comprising >81% of the total European urban population). Urban areas are defined spatially and demographically by the Global Rural Urban Mapping Project. We compare these results with "bottom-up" carbon accounting method results for cities in the region. Our results suggest that direct (Scopes 1 and 2) GHG emissions from urban areas range between 44 and 54% of total anthropogenic emissions for the region. While individual urban GHG footprints vary from bottom-up studies, both the mean differences and the regional energy-related GHG emission share support previous findings. Correlation analysis indicates that the urban GHG emissions in Europe are mainly influenced by population size, density, and income and not by biophysical conditions. We argue that these data and methods of analysis are best used at the regional or higher scales.

What Is Urban? Comparing a Satellite View with the Demographic and Health Surveys

Appraisal of urbanization trends is limited by the lack of a globally consistent definition of what is meant by urban. This article seeks to identify and explain differences in the definition of “urbanness” as used in two largely distinct research communities. We compare the Global Rural–Urban Mapping Project (GRUMP), which defines urban areas based primarily on satellite imagery of nighttime lights, to the urban classification found in Demographic and Health Surveys (DHS), which relies on the urban definitions of individual countries' national statistical offices. We analyze the distribution of DHS clusters falling within and outside of GRUMP urban extents and examine select characteristics of these clusters (notably, household electrification). Our results show a high degree of agreement between the two data sources on what areas are considered urban; furthermore, when used together, GRUMP and DHS data reveal urban characteristics that are not evident when one data source is used independently. GRUMP urban extents are overwhelmingly medium and large highly electrified localities. DHS clusters that are classified as non‐urban but that fall within GRUMP extents tend to be peri‐urban areas.

Statistical Change Detection in Water Cycle over Two Decades and Assessment of Impact of Urbanization on Surface and Sub-Surface Water Flows

Urbanization is termed as physical transformation of landscapes that alter the natural regime of the environment of its surrounding resulting in further changes in macro as well as micro climate of the region [1]. Urban areas are continually facing problems of water scarcity and urban flash floods. Recent news from IPCC report 2010, CIESIN’s Global Rural Urban Mapping Project [2] and World Water Vol. 7 2007, it is clear that urban land area has doubled and affected the hydrological cycle. The components of hydrological cycle affected are Infiltration, Runoff and Evaporation and the causing components are derived by studies as land use, land cover, water withdrawal and urban developments. Thus water availability, water recharge and water cycle are all destabilized in course or urban development. The paper is an attempt to correlate and identify the periodical changes in urban water cycle, during urbanization of Bhopal City, India, during last twenty years and above. The observations are based on GIS mapping of the study area from 1991 to 2009 using rational method of runoff and recharge calculations and statistical analysis of related built-up areas. Also change in natural course of drainages with the help of GIS imageries which have been detected during twenty years that help to observe the adaptation of natural system to urban course. Also the observations show an interesting relation which can be used for further research and sustainable development [3].

Generation of fine-scale population layers using multi-resolution satellite imagery and geospatial data

A gridded population dataset was produced for Pakistan by developing an algorithm that distributed population either on the basis of per-pixel built-up area fraction or the per-pixel value of a weighted population likelihood layer. Per-pixel built-up area fraction was calculated using a classification and regression trees (CART) methodology integrating high- and medium-resolution satellite imagery. The likelihood layer was produced by weighting different geospatial layers according to their effect on the likelihood of population being found in the particular pixel. The geospatial layers integrated into the likelihood layer were: 1) proximity to remotely sensed built-up pixels, 2) density of settlement points in a fixed kernel, 3) slope, 4) elevation, and 5) heterogeneity of landcover types found within a search radius. The method for weighting these layers varied according to settlement patterns found in the provinces of Pakistan. Differences in zonal population estimates generated from the 100-meter gridded population layer resulting from this study, Oak Ridge National Laboratory's LandScan (2002), and CIESIN's Gridded Population of the World and Global Rural Urban Mapping Project (GPW and GRUMP) are examined. Population estimates for small areas produced using this paper's method were found to differ from census counts to a lesser degree than those produced using LandScan, GPW, or GRUMP. The root mean square error (RMSE) for small area population estimates for this method, LandScan, GPW, and GRUMP were 31,089, 48,001, 100,260, and 72,071, respectively.

Uncertainties in measuring populations potentially impacted by sea level rise and coastal flooding.

A better understanding of the impact of global climate change requires information on the locations and characteristics of populations affected. For instance, with global sea level predicted to rise and coastal flooding set to become more frequent and intense, high-resolution spatial population datasets are increasingly being used to estimate the size of vulnerable coastal populations. Many previous studies have undertaken this by quantifying the size of populations residing in low elevation coastal zones using one of two global spatial population datasets available – LandScan and the Global Rural Urban Mapping Project (GRUMP). This has been undertaken without consideration of the effects of this choice, which are a function of the quality of input datasets and differences in methods used to construct each spatial population dataset. Here we calculate estimated low elevation coastal zone resident population sizes from LandScan and GRUMP using previously adopted approaches, and quantify the absolute and relative differences achieved through switching datasets. Our findings suggest that the choice of one particular dataset over another can translate to a difference of more than 7.5 million vulnerable people for countries with extensive coastal populations, such as Indonesia and Japan. Our findings also show variations in estimates of proportions of national populations at risk range from <0.1% to 45% differences when switching between datasets, with large differences predominantly for countries where coarse and outdated input data were used in the construction of the spatial population datasets. The results highlight the need for the construction of spatial population datasets built on accurate, contemporary and detailed census data for use in climate change impact studies and the importance of acknowledging uncertainties inherent in existing spatial population datasets when estimating the demographic impacts of climate change.

Sickle-cell disorders: limits of descriptive epidemiology

The global mapping of sickle haemoglobin (HbS) allele frequencies in neonates at a fine-grained 5×5 km resolution, undertaken by Frédéric Piel and colleagues 1 Piel FB Patil AP Howes RE et al. Global epidemiology of sickle haemoglobin in neonates: a contemporary geostatistical model-based map and population estimates. Lancet. 2012; (Published online Oct 25.)http://dx.doi.org/10.1016/S0140-6739(12)61229-X PubMed Google Scholar in The Lancet, uses a new approach extended from work on malaria (with which haemoglobin variation is closely associated 2 Piel FB Patil AP Howes RE et al. Global distribution of the sickle cell gene and geographical confirmation of the malaria hypothesis. Nat Commun. 2010; 1: 104 Crossref PubMed Scopus (302) Google Scholar , 3 Firdous N Gibbons S Modell B Falling prevalence of beta-thalassaemia and eradication of malaria in the Maldives. J Community Genet. 2011; 2: 173-189 Crossref PubMed Scopus (16) Google Scholar ), and thus adds a powerful new instrument to the haemoglobin research community's armamentarium. The contribution is multifaceted: updating the literature review to 2010; applying a rigorous method including use of the global rural urban mapping project (GRUMP); and publication of results in a prestigious journal, which highlights the position of sickle-cell disorders as a global health concern. 4 WHO59th World Health Assembly resolution WHA59.20 on sickle cell anaemia. http://apps.who.int/gb/ebwha/pdf_files/WHA59-REC1/e/Resolutions-en.pdf Google Scholar Although the coverage is currently limited to HbS, its extension, in time, to other alleles that interact with HbS to cause sickle-cell disorders (principally haemoglobin C and β-thalassaemias) will greatly enrich the outputs. Global epidemiology of sickle haemoglobin in neonates: a contemporary geostatistical model-based map and population estimatesHbS will have an increasing effect on public health systems. Our estimates can help countries and the international community gauge the need for appropriate diagnoses and genetic counselling to reduce the number of neonates affected. Similar mapping and modelling methods could be used for other inherited disorders. Full-Text PDF Open AccessBoosting services for people with sickle-cell diseaseThe Igbo people of Nigeria called them Ogbanje—malevolent children destined to be lazy and die and be reborn repeatedly. Particular families were plagued by such children who usually died before their 10th birthday. Belief in Ogbanje is centuries old, but a little more than 100 years ago, a less ethereal explanation for the Ogbanje was found. Walter Clement Noel was of African descent and living in America. He had anaemia and chest pains, and in 1910 doctors Ernest Irons and James Herrick looked at his blood under a microscope. Full-Text PDF

Inconsistent definitions of “urban” result in different conclusions about the size of urban carbon and nitrogen stocks

There is conflicting evidence about the importance of urban soils and vegetation in regional C budgets that is caused, in part, by inconsistent definitions of "urban" land use. We quantified urban ecosystem contributions to C stocks in the Boston (Massachusetts, USA) Metropolitan Statistical Area (MSA) using several alternative urban definitions. Development altered aboveground and belowground C and N stocks, and the sign and magnitude of these changes varied by land use and development intensity. Aboveground biomass (live trees, dbh > or = 5 cm) for the MSA was 7.2 +/- 0.4 kg C/m2 (mean +/- SE), reflecting a high proportion of forest cover. Vegetation C was highest in forest (11.6 +/- 0.5 kg C/m2), followed by residential (4.6 +/- 0.5 kg C/m2), and then other developed (2.0 +/- 0.4 kg C/m2) land uses. Soil C (0-10 cm depth) followed the same pattern of decreasing C concentration from forest, to residential, to other developed land uses (4.1 +/- 0.1, 4.0 +/- 0.2, and 3.3 +/- 0.2 kg C/m2, respectively). Within a land use type, urban areas (which we defined as > 25% impervious surface area [ISA] within a 1-km(2) moving window) generally contained less vegetation C, but slightly more soil C, than nonurban areas. Soil N concentrations were higher in urban areas than nonurban areas of the same land use type, except for residential areas, which had similarly high soil N concentrations. When we compared our definition of urban to other commonly used urban extents (U.S. Census Bureau, Global Rural-Urban Mapping Project [GRUMP], and the MSA itself), we found that urban soil (1 m depth) and vegetation C stocks spanned a wide range, from 14.4 +/- 0.8 to 54.5 +/- 3.4 Tg C and from 4.2 +/- 0.4 to 27.3 +/- 3.2 Tg C, respectively. Conclusions about the importance of urban soils and vegetation to regional C and N stocks are very sensitive to the definition of urban used by the investigators. Urban areas, regardless of definition, are rapidly expanding in their extent; a systematic understanding of how our development patterns influence ecosystems is necessary to inform future development choices.

Development of a New Ground Truth Database for Global Urban Area Mapping from a Gazetteer

We developed a ground truth database for urban areas from the Global Rural-Urban Mapping Project (GRUMP) Settlement Points gazetteer of populated place names by visually interpreting 3,734 urban points on satellite images, thus acquiring 2,144 urban and 1,388 non-urban data points. Our database contained many more urban data points than the existing databases, which had only 0 to 11 ground truth data points. We used our database in combination with the Degree Confluence Project database to assess the accuracy of eight satellite-derived urban area maps, among which the MODIS Terra + Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid was the most accurate (84% overall accuracy; kappa coefficient, 0.63). Moreover, the most recently published maps were not necessarily the most accurate. We compared the accuracy assessment results of our database with those of another database and found that ours detected more errors of commission but included less chance agreement.

Human population, urban settlement patterns and their impact on Plasmodium falciparum malaria endemicity.

BackgroundThe efficient allocation of financial resources for malaria control and the optimal distribution of appropriate interventions require accurate information on the geographic distribution of malaria risk and of the human populations it affects. Low population densities in rural areas and high population densities in urban areas can influence malaria transmission substantially. Here, the Malaria Atlas Project (MAP) global database of Plasmodium falciparum parasite rate (PfPR) surveys, medical intelligence and contemporary population surfaces are utilized to explore these relationships and other issues involved in combining malaria risk maps with those of human population distribution in order to define populations at risk more accurately.MethodsFirst, an existing population surface was examined to determine if it was sufficiently detailed to be used reliably as a mask to identify areas of very low and very high population density as malaria free regions. Second, the potential of international travel and health guidelines (ITHGs) for identifying malaria free cities was examined. Third, the differences in PfPR values between surveys conducted in author-defined rural and urban areas were examined. Fourth, the ability of various global urban extent maps to reliably discriminate these author-based classifications of urban and rural in the PfPR database was investigated. Finally, the urban map that most accurately replicated the author-based classifications was analysed to examine the effects of urban classifications on PfPR values across the entire MAP database.ResultsMasks of zero population density excluded many non-zero PfPR surveys, indicating that the population surface was not detailed enough to define areas of zero transmission resulting from low population densities. In contrast, the ITHGs enabled the identification and mapping of 53 malaria free urban areas within endemic countries. Comparison of PfPR survey results showed significant differences between author-defined 'urban' and 'rural' designations in Africa, but not for the remainder of the malaria endemic world. The Global Rural Urban Mapping Project (GRUMP) urban extent mask proved most accurate for mapping these author-defined rural and urban locations, and further sub-divisions of urban extents into urban and peri-urban classes enabled the effects of high population densities on malaria transmission to be mapped and quantified.ConclusionThe availability of detailed, contemporary census and urban extent data for the construction of coherent and accurate global spatial population databases is often poor. These known sources of uncertainty in population surfaces and urban maps have the potential to be incorporated into future malaria burden estimates. Currently, insufficient spatial information exists globally to identify areas accurately where population density is low enough to impact upon transmission. Medical intelligence does however exist to reliably identify malaria free cities. Moreover, in Africa, urban areas that have a significant effect on malaria transmission can be mapped.