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Large-scale crop phenology monitoring is essential for agro-ecosystem policy. Remote sensing helps track crop development but requires high-temporal and spatial resolutions. While datasets with both attributes are now available, their large-scale applications require significant resources. Medium-resolution data offer daily observations but lack detail for smaller plots. This study generated crop-specific phenomaps for mainland France (2016–2020) using PROBA-V data. A spatial disaggregation method reconstructed NDVI time series for individual crops within mixed pixels. Then, phenometrics were extracted from disaggregated PROBA-V and Sentinel-2 separately and compared to observed phenological stages. Results showed that PROBA-V-based phenomaps closely matched observations at regional level, with moderate accuracy at municipal level. PROBA-V demonstrated a higher detection rate than Sentinel-2, especially in cloudy periods, and successfully generated phenomaps before Sentinel-2B's launch. The study highlights PROBA-V's potential for operational crop monitoring, i.e., wheat heading and oilseed rape flowering, with performance comparable to Sentinel-2. PROBA-V outputs complement Sentinel-2: phenometrics cannot be generated at plot level but are efficiently produced at regional or national scales to study phenological gradients more easily than with Sentinel-2 and with similar accuracy. This approach could be extended to MODIS or SPOT-VGT, to generate historical phenological data, providing that a crop map is available. [ABSTRACT FROM AUTHOR]

Agricultural land use and management fundamentally impacts the condition of natural resources like waterbodies, soils, and biodiversity. Modelling the anthropogenic effects on those resources over time requires detailed knowledge of the temporal and spatial distribution of crops. However, currently available crop type maps for Europe either lack the required spatial resolution or the temporal and spatial coverage. We develop and apply a probabilistic, spatially explicit crop type mapping approach that is suitable for ex-post and ex-ante modelling. The approach allows to quantify epistemic and aleatoric uncertainty related to estimated crop shares by providing an ensemble of maps. We implement the method for the EU-28 for the years 2010 – 2020, distinguishing between 28 different crop types at 1 km resolution. Based on a model of the data generating process that conceptually links field-, grid cell- and region-level crop acreages, our approach considers soil, climate, and topography information, as well as administrative data. The validation with ground-truthing data for France indicates that the generated crop type maps are plausible. The provided uncertainty intervals capture differences in uncertainty across space and time and correctly identify grid cells and crops where estimations are less precise. The generated maps constitute a unique data product of high practical value, e.g., for agri-environmental modelling applications. We see additional potential in using the approach to disaggregate the regional or national predictions of socio-economic ex-ante prediction models.

Large-scale agent-based microsimulation platforms, increasingly used in transportation demand modelling, require fully enumerated and spatialized lists of the population and its sociodemographic characteristics as input. The quality of the synthetic population, measured as its ability to reproduce the sociodemographic characteristics of the real population and their spatial distributions, is thus a determinant factor of the model reliability. While many efforts were devoted to improving the sociodemographic accuracy of synthetic populations, less attention was paid to perfecting their spatial precision. Conventional spatialized population synthesis methods, where the generation and spatialization processes are separated, are vulnerable to inconsistencies between zonal synthetic populations, and the built environments on which they are then distributed. These methods also present transferability issues that lie in their high reliance on rich spatialized datasets and knowledge of the local context. Hence, we propose an integrated multiresolution framework (IMF) that overcomes the limitations of the conventional framework (CF) by its ability to directly generate synthetic populations at the building resolution with minimal data requirements. The IMF includes an extension of an optimization-based method to multiresolution applications where any number and aggregation of spatial resolutions can efficiently be controlled. The CF and the IMF are applied to generate synthetic populations for Montreal, Canada. We define and measure sociodemographic accuracy, spatial precision, overall quality, and building-resolution fit of the synthetic populations to compare the frameworks' performances. Despite a small loss in accuracy, the IMF achieves drastically better spatial precision, overall quality and building-resolution fit of synthetic populations, compared to the CF. [ABSTRACT FROM AUTHOR]

Tourism in Mexico is one of the most important economic activities, however, there isn’t any rigorously and systematically estimated data on the tourism gross domestic product (GDP) at the locality, municipality, or federal entity levels, which does not allow obtaining basic information for the construction of regional indicators and have more real knowledge about subnational tourist activity in the country. Hence, the general objectives that we intend to achieve in this research are: 1) to estimate the tourism GDP (GDPT) by states and municipalities for the year 2018 and its spatial distribution at the subnational level; 2) to calculate the tourism GDP by locality based on municipal and state GDPT estimates. Both estimates are carried out through rigorous econometric analyzes to provide certainty about their measurement and present the spatial distribution of tourist activity in the main towns, municipalities, and federal entities of Mexico. The empirical results obtained consist of data for 2018 of the tourism GDP for 828 localities, which participate with 90% of the national tourism GDP (GDPTN), 644 municipalities with 97% of the GDPTN, and 100% of the GDPTN for the 32 states of Mexico. [ABSTRACT FROM AUTHOR]

This study generates a daily temperature and precipitation dataset over Vietnam at a high resolution of 0.1° for the historical period 1980–2005 and the future period 2006–2100 under four representative concentration pathway (RCP) scenarios, namely RCP2.6, RCP4.5, RCP6.0, and RCP8.5. The bias correction (BC) and spatial disaggregation (SD) method is applied to the outputs of 31 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to create the new dataset called CMIP5-VN. To guide the BC and SD steps, gridded temperature and precipitation data interpolated from daily observations of 147 and 481 stations respectively are used. Results with the CMIP5-VN show that warming over Vietnam is projected to continue till the end of the 21st century under all four RCPs. The average temperature is projected to increase by 1.3±0.52 °C under RCP2.6 and by 3.85±0.85 °C under RCP8.5 between 2080–2099 and 1986–2005. The future increase is more intense in the northern regions than in the south and higher in summer than in winter. Precipitation is projected to increase by 1.16±7.1% under RCP2.6 and by 4.41±9.2% under RCP8.5. In Central Vietnam, there is a consistent rainfall increase in the future rainy season. HIGHLIGHTS This is the first time a complete set of high-resolution temperature and precipitation data for different scenarios has been created for Vietnam.; Besides RCP4.5 and RCP8.5, this is the first time the detailed changes under the RCP2.6 and RCP6.0 scenarios in the sub-regions of Vietnam are assessed.; The warming is more intense in the northern regions than in the south and higher in summer than winter for all RCPs.; Projected temperature changes relative to the baseline period 1986–2005 based on the newly-created CMIP5-VN data for Vietnam. Five-year moving averages are applied. Colored lines show the ensemble means of the models, and colored shaded areas present the uncertainty ranges (±1 standard deviation) for each representative concentration pathway (RCP) scenario. The number of models used for each RCP is shown in brackets. Box plots display the occurrence statistics (the ends of the box are the upper and lower quartiles, the horizontal line inside the box marks the median, and the two horizontal lines outside of the box indicate the 10th and 90th percentile values, respectively) for warming levels at the end of the 21st century.;

Precise estimates of vehicular emissions at fine spatial scales are essential for effective emission reduction strategies. Achieving high-resolution vehicular emission inventories necessitates detailed data on traffic flow, driving patterns, and vehicle speeds for each road network segment. However, in developing countries, the lack of comprehensive traffic data, limited infrastructure, and insufficient monitoring systems constrains the development of high-resolution inventories. This gap poses significant challenges for accurately quantify emissions in regions that often experience rapid urbanisation and traffic growth. Here, we propose a novel method to enhance the spatial disaggregation of large-scale vehicular emission inventories. By analysing road-level emissions data from 63 Brazilian municipalities, we developed a model that predicts weighting factors to disaggregate vehicular emissions into a gridded format, based on the proportion of primary road lengths. Our findings indicate that the predicted weighting factors significantly improve the spatial disaggregation of vehicular emissions compared to the traditional road density method by reasonably increasing the emissions in high vehicular activity areas. This approach not only provides more accurate representations of vehicular emissions for urban planning in Brazil but also offers a solution that can be adapted to enhance top-down vehicular emissions inventories globally. Our study offers a valuable tool that can be tailored to various regions, enabling more precise urban planning and policy-making for air quality management worldwide.

Agricultural land use and management fundamentally impacts the condition of natural resources like waterbodies, soils, and biodiversity. Modelling the anthropogenic effects on those resources over time requires detailed knowledge of the temporal and spatial distribution of crops. However, currently available crop type maps for Europe either lack the required spatial resolution or the temporal and spatial coverage. We develop and apply a probabilistic, spatially explicit crop type mapping approach that is suitable for ex-post and ex-ante modelling. The approach allows to quantify epistemic and aleatoric uncertainty related to estimated crop shares by providing an ensemble of maps. We implement the method for the EU-28 for the years 2010 – 2020, distinguishing between 28 different crop types at 1 km resolution. Based on a model of the data generating process that conceptually links field-, grid cell- and region-level crop acreages, our approach considers soil, climate, and topography information, as well as administrative data. The validation with ground-truthing data for France indicates that the generated crop type maps are plausible. The provided uncertainty intervals capture differences in uncertainty across space and time and correctly identify grid cells and crops where estimations are less precise. The generated maps constitute a unique data product of high practical value, e.g., for agri-environmental modelling applications. We see additional potential in using the approach to disaggregate the regional or national predictions of socio-economic ex-ante prediction models. [Display omitted] • Existing crop type maps for the EU lack required resolution and coverage. • We develop and apply a probabilistic crop mapping approach for the EU (2010−2020). • Model design enables application for ex-post estimation and ex-ante simulations. • An ensemble of maps quantifies epistemic and aleatoric uncertainty. • Comparison with observed crop shares for France shows generated maps are plausible. [ABSTRACT FROM AUTHOR]

Incidence angle diversity of space-borne radiometer and radar systems operating at low microwave frequencies needs to be taken into consideration to accurately estimate soil moisture (SM) across spatial scales. In this study, the single channel algorithm (SCA) is first applied to Soil Moisture and Ocean Salinity (SMOS) brightness temperatures at vertical polarization (${\mathrm{ TB}}_{V}$) to estimate SM at coarse resolution (25 km) and develop a land cover-specific and incidence angle (32.5°, 42.5°, and 52.5°)-adaptive calibration of single scattering albedo ($\omega$) and soil roughness ($h_{s}$) parameters. These effective parameters are used together with fine-scale multiangular Sentinel-1 backscatter in a single-pass active–passive downscaling approach to estimate ${\mathrm{ TB}}_{V}$ at fine scale (1 km) for each SMOS incidence angle. These ${\mathrm{ TB}}_{V}\mathrm {s}$ are finally inverted to obtain the corresponding high-resolution SM maps. Results over the Iberian Peninsula for year 2018 show an increasing trend of $\omega $ and a decreasing trend of $h_{s}$ with SMOS incidence angle, with almost no variability of $\omega $ across land cover types. The active–passive covariation parameter is shown to increase with SMOS incidence angle and decrease with Sentinel-1 incidence angle. Coarse and fine ${\mathrm{ TB}}_{V}$ maps from the three SMOS incidence angles show similar distributions (mean differences below 0.38 K). Resulting high-resolution SM maps have maximum differences in mean and standard deviation of 0.016 and 0.015 m3/m3, respectively, and compare well with in situ measurements. Our results indicate that model-based microwave approaches to estimate SM can be adequately adapted to account for the incidence angle diversity of planned missions, such as Copernicus Microwave Imaging Radiometer (CIMR), Radar Observing System for Europe in L-band (ROSE-L), and Sentinel-1 next generation. [ABSTRACT FROM AUTHOR]

Spatially explicit crop yield datasets with continuous long-term series are essential for understanding the spatiotemporal variation of crop yield and the impact of climate change on it. There are several spatial disaggregation methods to generate gridded yield maps, but these either use an oversimplified approach with only a couple of ancillary data or an overly complex approach with limited flexibility and scalability. This study developed a spatial disaggregation method using improved spatial weights generated from machine learning. When applied to Chinese maize yield, extreme gradient boosting (XGB) derived the best prediction results, with a cross-validation coefficient of determination (R2) of 0.81 at the municipal level. The disaggregated yield at 1 km grids could explain 54% of the variance of the county-level statistical yield, which is superior to the existing gridded maize yield dataset in China. At the site level, the disaggregated yields also showed much better agreement with observations than the existing gridded maize yield dataset. This lightweight method is promising for generating spatially explicit crop yield datasets with finer resolution and higher accuracy, and for providing necessary information for maize production risk assessment in China under climate change. [ABSTRACT FROM AUTHOR]

Satellite-based monitoring of crop phenology is commonly built on the analysis of Vegetation Index (VI) time series by extracting phenological metrics. The relatively fine detection of the various timings in crops growth during their development cycle depends on the density and regularity of valid observations. Medium spatial resolution (MSR) daily observations provide consistent cloud-free n-day composite time series, suitable for phenological applications, but do not offer an adequate spatial resolution. MSR pixels are generally mixed pixels or “mixels”, composed of several land cover classes, which complicate crop-specific monitoring from space. To address the MSR mixel problem, we implemented a spatial disaggregation (SD) approach that estimates a crop-specific VI based on the crop fraction in a mixel provided by a land use map. First, SD was applied on synthetic MSR data (i.e. Sentinel-2 data aggregated at 300 m) in order to test the method in an ideal case. After validation, the method was applied to PROBA-V data, using 300 m and 10-day composites over a large area around Paris, for four main crops (i.e. winter cereals, spring barley, oilseed rape and maize) in 2016–2017. The evaluation of SD was done by comparing disaggregated data with reference data (i.e. Sentinel-2 10 m). Indeed, two main results were observed, i) SD was able to reconstruct the crop-specific VI time series of all crops and ii) PROBA-V data increased the number of crop-specific VI valid observations at certain stages of the crop's growth period compared to Sentinel-2 data, this with a consistent and regular revisit throughout the growth cycle. In conclusion, SD can be used to improve the exploitation of MSR data in seasonal crop monitoring, especially during the transition periods when the VI of crops are likely to change quickly. This paves the way for monitoring crop phenology over fragmented landscapes, from sensors such as MODIS or SPOT-VEGETATION, even for years before Sentinel-2 launch.

In this paper we propose a methodology to obtain social indicators at a detailed spatial scale by combining the information contained in census and sample surveys. Similarly to previous proposals, the method proposed here estimates a model at the sample level to later project it to the census scale. The main novelties of the technique presented are that (i) the small-scale mapping produced is perfectly consistent with the aggregates -regional or national- observed in the sample, and (ii) it does not require imposing strong distributional assumptions. The methodology suggested here follows the basics presented on Golan (2018) by adapting a cross-moment constrained Generalized Maximum Entropy (GME) estimator to the spatial disaggregation problem. This procedure is compared with the equivalent methodology of Tarozzi and Deaton (2009) by means of numerical experiments, providing a comparatively better performance. Additionally, the practical implementation of the methodology proposed is illustrated by estimating poverty rates for small areas for the region of Andalusia (Spain). [ABSTRACT FROM AUTHOR]

The disaggregation technique must be applied in certain cases when information only available at a high-level spatial unit, to draw any conclusion for the lower level spatial unit. This problem commonly occurs in the area of regional economics, agricultural economics or environment study. One of the disaggregation techniques, namely entropy-based distributionally weighted regression is particularly the main interest in this study. It conducts a simulation study to analyze the effect of the explanatory power of the predictor, and the type of correlation between response and weight variable, on the accuracy of the method. The simulation study shows that the technique works well for predictors with at least medium explanatory power. The use of negatively correlated weight will not lead to a serious accuracy problem, as long as the correlation is kept at a low degree. [ABSTRACT FROM AUTHOR]

Area-to-point kriging (ATPK) is a geostatistical method for creating high-resolution raster maps using data of the variable of interest with a much lower resolution. The data set of areal means is often considerably smaller ( < 50 observations) than data sets conventionally dealt with in geostatistical analyses. In contemporary ATPK methods, uncertainty in the variogram parameters is not accounted for in the prediction; this issue can be overcome by applying ATPK in a Bayesian framework. Commonly in Bayesian statistics, posterior distributions of model parameters and posterior predictive distributions are approximated by Markov chain Monte Carlo sampling from the posterior, which can be computationally expensive. Therefore, a partly analytical solution is implemented in this paper, in order to (i) explore the impact of the prior distribution on predictions and prediction variances, (ii) investigate whether certain aspects of uncertainty can be disregarded, simplifying the necessary computations, and (iii) test the impact of various model misspecifications. Several approaches using simulated data, aggregated real-world point data, and a case study on aggregated crop yields in Burkina Faso are compared. The prior distribution is found to have minimal impact on the disaggregated predictions. In most cases with known short-range behaviour, an approach that disregards uncertainty in the variogram distance parameter gives a reasonable assessment of prediction uncertainty. However, some severe effects of model misspecification in terms of overly conservative or optimistic prediction uncertainties are found, highlighting the importance of model choice or integration into ATPK. [ABSTRACT FROM AUTHOR]

An accurate spatial disaggregation method is crucial to ensure the accuracy of gridding vehicle emissions inventories. In the present study, we proposed a novel spatial disaggregation model that combines standard road length with aerosol optical depth (AOD) remote sensing data. Qingdao, a typical coastal city in China, was selected as the study area. We used the new model to obtain a gridded vehicle emissions inventory with a resolution of 1 × 1 km. In the novel model, the square of the correlation coefficient between the disaggregated PM 2.5 emission and measured PM 2.5 increased by approximately 10%. The normalized mean deviation and error between the simulated PM 2.5 determined using the Community Multi-scale Air Quality (CMAQ) model decreased by 35% and 27%, respectively, compared with those determined using the standard road length method. The focus of urban traffic control is to address the hotspot formation of high-emission areas in the Jiaozhou Bay region. This study expands the potential application of remote sensing data and improves the accuracy of vehicle emissions inventories. • A novel spatial disaggregation model based on AOD improves the accuracy of gridded vehicle emissions inventory. • The R2 of the disaggregated PM 2.5 emission based on the novel model and the measured PM 2.5 increased by approximately 10%. • The area around Jiaozhou Bay is a high PM 2.5 emission area for vehicles. [ABSTRACT FROM AUTHOR]

In South Africa, the only soil resource available with full spatial coverage is the national resource inventory. Disaggregating this polygon-based inventory, is thus a logical step to create more detailed soil maps covering the entire country. The polygons are large in area encompassing complex soil-terrain patterns and research into disaggregation techniques has been limited. This study aimed to compare 10 algorithms, implemented through a modified DSMART ("Disaggregating and Harmonizing Soil Map Units Through Resampled Classification Trees") model, in their ability to disaggregate two polygons into soil associations in two environmentally contrasting locations. One site had high relief and strong catenal sequences (eastern KwaZulu-Natal Province) and the other site had low relief and a strong geological control of soil types (northern Eastern Cape Province). The algorithms compared were based on previous studies which included k-nearest neighbour, nearest shrunken centroid, discriminatory analysis, multinomial logistics regression, linear and radial support vector machines, decision trees, stochastic gradient boosting, random forest, and neural networks. The method involves stratifying the polygons with landform elements, randomly sampling the landform elements, allocating the soil classes based on the resource inventory, and predicting soil associations across a stack of covariates. This was done in an iterative process, creating multiple realisations of the soil distribution. The performance of each algorithm was based on their kappa and uncertainties. It was found that in general, robust linear models which either utilise an embedded feature selection or regularise covariates, performed best. In the area with high relief and clear toposequences, nearest shrunken centroid was the top performing algorithm with a kappa of 0.42 and an average uncertainty of 0.22. In the area with relatively low relief and complex geology, the results were unsatisfactory. However, a regularised multinomial regression was the top performing algorithm, achieving a kappa of 0.17 and an average uncertainty of 0.84. The results of this study highlight the versatility of a technique to disaggregate South Africa's national resource inventory, where algorithms can be chosen on expert knowledge, model averaging can be performed, the top performing algorithm can be chosen, and algorithm parameters can be optimised. • Ten algorithms were compared in diaggregating a national resource inventory. • The algorithms were compared through a modified DSMART model. • Robust linear algorithms, in general, achieved the best results. • In an area with clear toposequences, the model achieved satisfactory results. • In an area with more soil-geological relationships, the model needs improvement. [ABSTRACT FROM AUTHOR]

The National Science Foundation sponsored a workshop on status and trends in spatial analysis from December 10­-12, 1998 organized under the Varenius program, the National Center for Geographic Information and Analysis (NCGIA)’s project to advance geographic information science. The workshop focused on assessing how successful GIS has been at making spatial analysis widely available to physical and social scientists, and of what new directions might be researched in the future. This document includes a list of the participants involved in the workshop, as well as the position papers they submitted with their application.

In the last decade, technological advances led to the launch of two satellite missions dedicated to measure the Earth’s surface soil moisture (SSM): the ESA’s Soil Moisture and Ocean Salinity (SMOS) launched in 2009, and the NASA’s Soil Moisture Active Passive (SMAP) launched in 2015. The two satellites have an L-band microwave radiometer on-board to measure the Earth’s surface emission. These measurements (brightness temperatures TB) are then used to generate global maps of SSM every three days with a spatial resolution of about 30−40 km and a target accuracy of 0.04 m3/m3. To meet local applications needs, different approaches have been proposed to spatially disaggregate SMOS and SMAP TB or their SSM products. They rely on synergies between multi-sensor observations and are built upon different physical assumptions. In this study, temporal and spatial characteristics of six operational SSM products derived from SMOS and SMAP are assessed in order to diagnose their distinct features, and the rationale behind them. The study is focused on the Iberian Peninsula and covers the period from April 2015 to December 2017. A temporal inter-comparison analysis is carried out using in situ SSM data from the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) to evaluate the impact of the spatial scale of the different products (1, 3, 9, 25, and 36 km), and their correspondence in terms of temporal dynamics. A spatial analysis is conducted for the whole Iberian Peninsula with emphasis on the added-value that the enhanced resolution products provide based on the microwave-optical (SMOS/ERA5/MODIS) or the active−passive microwave (SMAP/Sentinel-1) sensor fusion. Our results show overall agreement among time series of the products regardless their spatial scale when compared to in situ measurements. Still, higher spatial resolutions would be needed to capture local features such as small irrigated areas that are not dominant at the 1-km pixel scale. The degree to which spatial features are resolved by the enhanced resolution products depend on the multi-sensor synergies employed (at TB or soil moisture level), and on the nature of the fine-scale information used. The largest disparities between these products occur in forested areas, which may be related to the reduced sensitivity of high-resolution active microwave and optical data to soil properties under dense vegetation.

High-resolution population grids built from historical census data can ease the analyses of geographical population changes, at the same time also facilitating the combination of population data with other GIS layers to perform analyses on a wide range of topics. This article reports on experiments with a hybrid spatial disaggregation technique that combines the ideas of dasymetric mapping and pycnophylactic interpolation, using modern machine learning methods to combine different types of ancillary variables, in order to disaggregate historical census data into a 200 m resolution grid. We specifically report on experiments related to the disaggregation of historical population counts from three different national censuses which took place around 1900, respectively in Great Britain, Belgium, and the Netherlands. The obtained results indicate that the proposed method is indeed highly accurate, outperforming simpler disaggregation schemes based on mass-preserving areal weighting or pycnophylactic interpolation. The best results were obtained using modern regression methods (i.e., gradient tree boosting or convolutional neural networks, depending on the case study), which previously have only seldom been used for spatial disaggregation.

Estuaries are productive and ecologically important ecosystems, incorporating environmental drivers from watersheds, rivers, and the coastal ocean. Climate change has potential to modify the physical properties of estuaries, with impacts on resident organisms. However, projections from general circulation models (GCMs) are generally too coarse to resolve important estuarine processes. Here, we statistically downscaled near-surface air temperature and precipitation projections to the scale of the Chesapeake Bay watershed and estuary. These variables were linked to Susquehanna River streamflow using a water balance model and finally to spatially resolved Chesapeake Bay surface temperature and salinity using statistical model trees. The low computational cost of this approach allowed rapid assessment of projected changes from four GCMs spanning a range of potential futures under a high CO2 emission scenario, for four different downscaling methods. Choice of GCM contributed strongly to the spread in projections, but choice of downscaling method was also influential in the warmest models. Models projected a ~2–5.5 °C increase in surface water temperatures in the Chesapeake Bay by the end of the century. Projections of salinity were more uncertain and spatially complex. Models showing increases in winter-spring streamflow generated freshening in the Upper Bay and tributaries, while models with decreased streamflow produced salinity increases. Changes to the Chesapeake Bay environment have implications for fish and invertebrate habitats, as well as migration, spawning phenology, recruitment, and occurrence of pathogens. Our results underline a potentially expanded role of statistical downscaling to complement dynamical approaches in assessing climate change impacts in dynamically challenging estuaries. [ABSTRACT FROM AUTHOR]

Digital soil mapping is becoming a powerful tool to increase the spatial detail of soil information over large areas, which is essential to address agronomic and environmental issues. When it exists, information about soil is often sparse or available at a coarser resolution than required. The spatial distribution of soil at the regional scale is usually represented as a set of polygons defining Soil Map Units (SMUs), each including several Soil Type Units (STUs), which are not spatially delineated but semantically described in a database. Delineation of STUs within SMUs, i.e. spatial disaggregation of SMU, should improve the precision of soil information derived from legacy and ancillary data. The aim of this study was to predict STUs by spatially disaggregating SMUs through a decision-tree approach that considered expert knowledge about soil-landscape relationships embedded in soil databases. In a 27,376 km 2 study area in north-western France (Brittany), 434 SMUs were delineated at 1:250,000 scale, and 320 STUs, their relative area in the SMUs, and their geomorphological and geological contexts were described. A calibration dataset of points was established using stratified random sampling (n = 352,188). To retrieve soil-landscape relationships, expert rules for soil distribution defined by soil surveyors and based on topography, parent material and waterlogging index were considered in order to allocate an STU to 83% of the calibration dataset. The calibration dataset and covariates (i.e. pedological, geological and terrain attributes; land use; airborne gamma-ray spectrometry) were then used to build and extrapolate the decision tree using the C.5 algorithm in DSMART software. Several iterations were performed, providing a probability of occurrence of each possible STU within the study area. External validation was performed by comparing predictions of the disaggregation procedure to available soil maps at scales of 1:25,000 or 1:50,000 and observed profiles. Overall accuracies ranged from 41 to 72%, depending on the validation method (per pixel vs. 3 × 3 windows of pixels, per STU vs. STU grouped by semantic proximity (n = 204)). Introducing expert rules based on soil-landscape relationships to allocate STUs to calibration samples enabled production of a soil map with clear spatial structures, yielding expected spatial patterns of soil organisation. Future work notably concerns estimating soil properties at multiple depths deriving from STU predictions, according to the GlobalSoilMap project. [ABSTRACT FROM AUTHOR]

The extent of economic losses due to a natural hazard and disaster depends largely on the spatial distribution of asset values in relation to the hazard intensity distribution within the affected area. Given that statistical data on asset value are collected by administrative units in China, generating spatially explicit asset exposure maps remains a key challenge for rapid postdisaster economic loss assessment. The goal of this study is to introduce a top-down (or downscaling) approach to disaggregate administrative-unit level asset value to grid-cell level. To do so, finding the highly correlated 'surrogate' indicators is the key. A combination of three data sets-nighttime light grid, LandScan population grid, and road density grid, is used as ancillary asset density distribution information for spatializing the asset value. As a result, a high spatial resolution asset value map of China for 2015 is generated. The spatial data set contains aggregated economic value at risk at 30 arc-second spatial resolution. Accuracy of the spatial disaggregation reflects redistribution errors introduced by the disaggregation process as well as errors from the original ancillary data sets. The overall accuracy of the results proves to be promising. The example of using the developed disaggregated asset value map in exposure assessment of watersheds demonstrates that the data set offers immense analytical flexibility for overlay analysis according to the hazard extent. This product will help current efforts to analyze spatial characteristics of exposure and to uncover the contributions of both physical and social drivers of natural hazard and disaster across space and time. [ABSTRACT FROM AUTHOR]

Technological innovation in transportation and the related industrial and entrepreneurial ecosystems can reduce urban environmental risks, maintaining urban environments. This study measures inter-county spillovers of the greenhouse gas (GHG) emissions impact associated with economic activity changes in the Southern California region when emerging clean transportation technologies are introduced to the region. Using a pseudo 'top-down' method, we successfully distributed the state level GHG emissions data to the county level because the GHG emissions inventory is not supported below the state level by the U.S. Environmental Protection Agency. Using the Environmental Southern California's Inter-county Social Accounting Matrix (ESCI-SAM) model and a bridge connecting two digit NAICS sectors to the GHG emissions inventory sectors, the spillover effects were measured by replacing of existing final demand for seven counties in the Southern California region. Furthermore, this study developed an extended method to discover how the effect of seven counties of the region is distributed to the rest of California excluding the seven counties, the rest of the U.S., and the rest of the world, respectively. While this study tried to measure how alternative, new clean technology freight vehicles that meet the goal of the Scoping Plan of California could spill over the region, the model developed in this study can be used for the diverse scenario simulations that involve an introduction of green economy that regulates GHG emissions of a local region. [ABSTRACT FROM AUTHOR]

This paper examines the recent application of the Geographical Information Systems (GIS) to the analysis of population distribution. We mention the efforts of the National Statistical Institutes in this direction boosted by the last census 2011.The stating point is a growing need to have available population figures for areas not related to administrative boundaries, either user defined zones or in grid format.This allows a convenient zonal system to combine demographic characteristics with environmental and pure geographic data, so the relation between the man and the environment can be analyzed in a unified way.Eventually, we offer a practical illustration of the interactions between GIS techniques and administrative population data in the study of spatial population distribution: We build a density grid for Spain by dasymetric methods from census tracts population data and Land Cover and Use Information System of Spain (SIOSE).The analysis is done within the spatial reference framework of the European Union.

The present paper attempts to establish a set of variables to find out the extent and levels of inequality in Human Development Indicators (HDIs) driven development based on a geographic delineation of Kolkata Urban Agglomeration (KUA). The study is based on the geographic delineation of the region of KUA. Given this background, it assesses health, education and economic scenario of the study area by variables of rates of mortality, literacy and working population, respectively. For the purpose of analysis, initially percentage distributions of variables over the study area have been computed and subsequently, assessed through Gini's Coefficient, which is an indicator of inequality in distribution over a geographic space. Then reviews of variations by virtue of spatial aggregation and disaggregation in the form of agglomerations of core vis-'-vis periphery, east bank versus west bank differentials have been presented. The analysis is supplemented by correlation studies. United Nations Development Program (UNDP) supports the fact that inequalities in development can be better addressed through assessment of qualitative indicators of Human Development Index. This very concept is also reflected in recent urban development researches conducted by organizations like Indian Council for Research on International Economic Relations (ICRIER, 2010), National Council of Applied Economic Research (NCAER, 2009) and London School of Economics (LSE, 2009). The organizations suggest that the holistic objective of India's metropolitan development can be best approached if objectives of minimization of income based inequalities proceed in its multiple dimensions like Human Development Indicators (HDIs). The present paper attempts to meet such goals in the perspective of KUA.

OpenStreetMap (OSM) database has previously been used to support spatial disaggregation of population data by partly masking out non-residential impervious areas in the European Copernicus imperviousness layer (IL). However, the exact procedure of OSM data incorporation is unknown, and its contribution to the improvement of estimation accuracy has never been studied. In this article, we present a sensitivity study to find out which road categories should be used for masking of IL and how the linear features might be transformed to raster representation. Using Austria and Slovenia as a study area, 2006 commune population counts are disaggregated into 100 m grid cells using 12 versions of modified IL. Further tuning of estimates is performed using CORINE Land Cover (CLC) data in an iterative algorithm. Disaggregated grids are then validated against reference 1 km census-based data. The results show that overall error was reduced thanks to OSM incorporation in all tested scenarios, although the relative improvement varies between as well as within the two countries. The best result (5.3% reduction) was achieved using railways and three major road categories (motorway, trunk, and primary) with double exaggeration of width. [ABSTRACT FROM AUTHOR]

Tehran with a population of 8.2 million urban residents, suffers from rapid urbanization in recent years resulting in severe air pollution. The aim of this study is to develop a high resolution emission inventory of primary air pollutants for Tehran. Tehran pollution sources are classified into two major categories. Mobile sources, including vehicles such as passenger car, taxi, motorcycle, pickup, minibus, bus and truck, and stationary sources; consisting of industries, general service and household, energy conversion, terminals and gas stations. The emission of SOx, NOx, CO, VOCs and PM in the year of 2013 were estimated as 37.411 kt, 85.524 kt, 506.690 kt, 83.640 kt and 8.496 kt, respectively. The results also indicate that mobile sources produced nearly 85% of the total aggregated pollutants while the stationary sources pollutants accounted for the remaining 15%. In a more elaborated view, 6.22% of SOx, 46.1% of NOx, 97.5% of CO, 86% of VOCs and 69.8% of PM were emitted from mobile sources while stationary sources produced the remaining amount of pollutants. [ABSTRACT FROM AUTHOR]

Researchers in the conflict research community have become increasingly aware that we can no longer depend on state-aggregated data. Numerous factors at the substate level affect the nature of human interactions, so if we really want to understand conflict, we need to find more appropriate units of analysis. However, while many conflict researchers have realized this, actually taking the next step and performing data analysis on spatial data grids has remained a rather elusive goal for many because of the difficulty of learning the new techniques to perform such analyses. This paper introduces SpatialGridBuilder, a new, freely available, open-source system with the goal of empowering conflict researchers with no background in GIS methods to start their own spatial analyses. SpatialGridBuilder allows the researcher to: (a) create entirely new spatial datasets, based on the needs of their own research; (b) import their own spatial data; (c) easily add a range of important variables to the datasets, including commonly used conflict variables, plus new variables that have not been presented before; and (d) visualize graphical renderings of this data. Having done this, SpatialGridBuilder will then export the dataset for the researcher to analyse using conventional statistical methods. This article introduces the new program, and demonstrates how it can be used to set up such a statistical analysis. It also shows how different results can be achieved by building grids of different resolutions, thereby encouraging researchers to choose grid resolutions appropriate to their research questions and data. The article also introduces a novel means of determining infrastructure complexity, using Google maps. [ABSTRACT FROM AUTHOR]

Changes of carbon stocks in agricultural soils, emissions of greenhouse gases from agriculture, and the delivery of ecosystem services of agricultural landscapes depend on combinations of land-use, livestock density, farming practices, climate and soil types. Many environmental processes are highly non-linear. If the analysis of the environmental impact is based on data at a relatively coarse-scale (e.g. farm, country, or large administrative regions), conclusions can be misleading. For an accurate assessment of agri-environmental indicators, data of agricultural activities and their dynamics are needed at high spatial resolution. In this paper, we develop and validate a spatial model for predicting the agricultural land-use areas within the homogenous spatial units (HSUs). For the EU-28 countries, we distinguish about 1.5 × 10 5 HSUs and we consider 30 possible land-uses to match with the classification used in the Common Agricultural Policy Regionalized Impact (CAPRI) model. The comparison of model predictions with independent observations and with a simple rule-based approach at HSU level demonstrates that the predictions are generally accurate in more than 75 % of HSUs. The frequent crops or land-use are better predicted. For non-frequent crops and/or crops requiring specific cultivation conditions, the model needs further fine-tuning. [ABSTRACT FROM AUTHOR]