Vector control strategies recommended by the World Health Organization are threatened by resistance of Anopheles mosquitoes to insecticides. Information on the distribution of resistant genotypes of ...malaria vectors is increasingly needed to address the problem. Ten years of published and unpublished data on malaria vector susceptibility/resistance and resistance genes have been collected across Togo. Relationships between the spatial distribution of resistance status and environmental, socio-economic, and landscape features were tested using randomization tests, and calculating Spearman rank and Pearson correlation coefficients between mosquito mortality and different gridded values. Anopheles gambiae sensu lato was resistant to DDT, pyrethroids, and the majority of carbamates and organophosphates. Three sibling species were found (i.e., An. gambiae, Anopheles coluzzii, and Anopheles arabiensis) with four resistance genes, including kdr (L1014F, L1014S, and N1575Y) and ace1 (G119S). The most frequent resistance gene was L1014F. Overall, no association was found between the susceptibility/resistance status and environmental features, suggesting that evolution of resistance may be most closely related to extreme selection from local insecticide use. Nevertheless, further research is necessary for firm conclusions about this lack of association, and the potential role of landscape characteristics such as presence of crops and percentage of tree cover.
Categorical raster datasets often require upscaling to a lower spatial resolution to make them compatible with the scale of ecological analysis. When aggregating categorical data, two critical issues ...arise: (a) ignoring compositional information present in the high‐resolution grid cells leads to high and uncontrolled loss of information in the scaled dataset; and (b) restricting classes to those present in the high‐resolution dataset assumes validity of the classification scheme at the lower, aggregated resolution.
I introduce a new scaling algorithm that aggregates categorical data while simultaneously controlling for information loss by generating a non‐hierarchical, representative, classification system for the aggregated scale. The Multi‐Dimensional Grid‐Point (MDGP) scaling algorithm acknowledges the statistical constraints of compositional count data. In a neutral‐landscape simulation study implementing a full‐factorial design for landscape characteristics, scale factors and algorithm parameters, I evaluated consistency and sensitivity of the scaling algorithm. Consistency and sensitivity were assessed for compositional information retention (IRcmp) and class‐label fidelity (CLF, the probability of recurring scaled class labels) for neutral random landscapes with the same properties.
The MDGP‐scaling algorithm consistently preserved information at a significantly higher rate than other commonly used algorithms. Consistency of the algorithm was high for IRcmp and CLF, but coefficients of variation of both metrics across landscapes varied most with class‐abundance distribution. A diminishing return for IRcmp was observed with increasing class‐label precision. Mean class‐label recurrence probability was consistently above 75% for all simulated landscape types, scale factors and class‐label precisions.
The MDGP‐scaling algorithm is the first algorithm that generates data‐driven, scale‐specific classification schemes while conducting spatial data aggregation. Consistent gain in IRcmp and the associated reproducibility of classification systems strongly suggest that the increased precision of scaled maps will improve ecological models that rely on upscaling of high‐resolution categorical raster data.
Marine spatial planning (MSP) has recently attracted more attention as an efficient decision support tool. MSP is a strategic and long-term process gathering multiple competing users of the ocean ...with the objective to simplify decisions regarding the sustainable use of marine resources. One of the challenges in MSP is to determine an optimal zone to locate a new activity while taking into account the locations of the other existing activities. Most approaches to spatial zoning are formulated as non-linear optimization models involving multiple objectives, which are usually solved using stochastic search algorithms, leading to sub-optimal solutions. In this paper, we propose to model the problem as a Multi-Objective Integer Linear Program. The model is developed for raster data and it aims at maximizing the interest of the area of the zone dedicated to the new activity while maximizing its spatial compactness. We study two resolution methods: first, a weighted-sum of the two objectives, and second, an interactive approach based on an improved augmented version of the ϵ-constraint method, AUGMECON2. To validate and study the model, we perform experiments on artificially generated data. Our experimental study shows that AUGMECON2 represents the most promising approach in terms of relevance and diversity of the solutions, compactness, and computation time.
The National Agricultural Statistics Service, the statistical arm of the US Department of Agriculture, and the Multi-Resolution Land Characteristics Consortium, a group of the US federal agencies, ...collect and publish several land-use and land-cover data sets. The aim of this study is to analyze the consistency of forestland estimates based on two widely used, publicly available products: the National Land-Cover Database (NLCD) and Cropland Data Layer (CDL). Both remote-sensing-based products provide raster-formatted land-cover categorization at a spatial resolution of 30 m. Although the processing of the yearly published CDL non-agricultural land-cover data is based on less frequently updated NLCD, the consistency of large-area forestland mapping between these two datasets has not been assessed. To assess the similarities and the differences between CDL- and NLCD-based forestland mappings for the state of North Carolina, we overlay the two data products for the years 2011 and 2016 in ArcMap 10.5.1 and analyze the location and attributes of the matched and mismatched forestland. We find that the mismatch is relatively smaller for the areas of the state where forests occupy larger shares of the total land, and that the relative mismatch is smaller in 2011 when compared to 2016. We also find that a large portion of the forestland mismatch is attributable to the dynamics of re-growth of periodically harvested and otherwise disturbed forests. Our results underscore the need for a holistic approach to data preparation, data attribution, and data accuracy when performing high-scale map-based analyses using each of these products.
Both the increasing number of GPS-enabled mobile devices and the geographic crowd-sourcing initiatives, such as Open Street Map, are determinants for the large amount of vector spatial data that is ...currently being produced. On the other hand, the automatic generation of raster data by remote sensing devices and environmental modeling processes was always leading to very large datasets. Currently, huge data generation rates are reached by improved sensor observation systems and data processing infrastructures. As an example, the Sentinel Data Access System of the Copernicus Program of the European Space Agency (ESA) was publishing 38.71 TB of data per day during 2020. This paper shows how the assumption of a new spatial data model that includes multi-resolution parametric spatial data types, enables achieving an efficient implementation of a large scale distributed spatial analysis system for integrated vector-raster data lakes. In particular, the proposed implementation outperforms the state-of-the-art Spark-based spatial analysis systems by more than one order of magnitude during vector-raster spatial join evaluation.
Scientific datasets from global-scale earth science models and remote sensing instruments are becoming available at greater spatial and temporal resolutions with shorter lag times. Water data are ...frequently stored as multidimensional arrays, also called gridded or raster data, and span two or three spatial dimensions, the time dimension, and other dimensions which vary by the specific dataset. Water engineers and scientists need these data as inputs for models and generate data in these formats as results. A myriad of file formats and organizational conventions exist for storing these array datasets. The variety does not make the data unusable but does add considerable difficulty in using them because the structure can vary. These storage formats are largely incompatible with common geographic information system (GIS) software. This introduces additional complexity in extracting values, analyzing results, and otherwise working with multidimensional data since they are often spatial data. We present a Python package which provides a central interface for efficient access to multidimensional water data regardless of the file format. This research builds on and unifies existing file formats and software rather than suggesting entirely new alternatives. We present a summary of the code design and validate the results using common water-related datasets and software.
This paper presents a formal framework for the representation of three-dimensional geospatial data and the definition of common geographic information system (GIS) spatial operations. We use the ...compact stack-based representation of terrains (SBRT) in order to model geological volumetric data, both at the surface and subsurface levels, thus preventing the large storage requirements of regular voxel models. The main contribution of this paper is fitting the SBRT into the geo-atom theory in a seamless way, providing it with a sound formal geographic foundation. In addition we have defined a set of common spatial operations on this representation using the tools provided by map algebra. More complex geoprocessing operations or geophysical simulations using the SBRT as representation can be implemented as a composition of these fundamental operations. Finally a data model and an implementation extending the coverage concept provided by the Geography Markup Language standard are suggested. Geoscientists and GIS professionals can take advantage of this model to exchange and reuse geoinformation within a well-specified framework.
As the largest irrigation area in northwest China, the middle and lower reaches of the Yarkant River basin are limited in economic development by the shortage of surface water resources and the ...increasing demand for groundwater resources from agriculture and industry, and the phenomenon of over-exploitation is becoming increasingly serious, which is not in line with the concept of sustainable development. Therefore, improving the efficiency of water resource utilization while curbing the trend of declining groundwater levels is an important issue that needs to be addressed in the middle and lower reaches of Yarkant at present, specifically, by establishing a distributed hydrological model MIKE SHE based on a soil texture dataset. The model efficiency coefficient Ens, the water balance coefficient (WB), the correlation coefficient r, and the relative error Re were selected to evaluate the model’s applicability. The results were: Ens = 0.84, WB = 0.80, and r = 0.96 for the annual scale runoff simulation and Ens = 0.85, RE = 0.61, and r = 0.96 for the monthly scale runoff simulation. The relative errors between the simulated and observed values of the typical observation wells were 3.45%, 1.59%, 2.52%, and 0.35%. According to the analysis of the soil parameters on the runoff sensitivity and groundwater table sensitivity, the saturated hydraulic conductivity had the greatest effect on the peak discharge. The results show that the MIKE SHE model has some applicability in the lower and middle reaches of the Yarkant River basin.
Raster type of forest inventory data with site and growing stock variables interpreted for small square-shaped grid cells are increasingly available for forest planning. In Finland, there are two ...sources of this type of lattice data: the multisource national forest inventory and the inventory that is based on airborne laser scanning (ALS). In both cases, stand variables are interpreted for 16 m × 16 m cells. Both data sources cover all private forests of Finland and are freely available for forest planning. This study analyzed different ways to use the ALS raster data in forest planning. The analyses were conducted for a grid of 375 × 375 cells (140,625 cells, of which 97,893 were productive forest). The basic alternatives were to use the cells as calculation units throughout the planning process, or aggregate the cells into segments before planning calculations. The use of cells made it necessary to use spatial optimization to aggregate cuttings and other treatments into blocks that were large enough for the practical implementation of the plan. In addition, allowing premature cuttings in a part of the cells was a prerequisite for compact treatment areas. The use of segments led to 5–9% higher growth predictions than calculations based on cells. In addition, the areas of the most common fertility classes were overestimated and the areas of rare site classes were underestimated when segments were used. The shape of the treatment blocks was more irregular in cell-based planning. Using cells as calculation units instead of segments led to 20 times longer computing time of the whole planning process than the use of segments when the number of grid cells was approximately 100,000.
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