Grid Cells Research Articles

Grid Cells in Cognition: Mechanisms and Function.

The activity patterns of grid cells form distinctively regular triangular lattices over the explored spatial environment and are largely invariant to visual stimuli, animal movement, and environment geometry. These neurons present numerous fascinating challenges to the curious (neuro)scientist: What are the circuit mechanisms responsible for creating spatially periodic activity patterns from the monotonic input-output responses of single neurons? How and why does the brain encode a local, nonperiodic variable-the allocentric position of the animal-with a periodic, nonlocal code? And, are grid cells truly specialized for spatial computations? Otherwise, what is their role in general cognition more broadly? We review efforts in uncovering the mechanisms and functional properties of grid cells, highlighting recent progress in the experimental validation of mechanistic grid cell models, and discuss the coding properties and functional advantages of the grid code as suggested by continuous attractor network models of grid cells.

A Hybrid Path Planning Technique for the Time-Efficient Navigation of Unmanned Vehicles in an Unconstrained Environment

Optimal path planning refers to choosing a collision-free, shortest, and smooth path, which is achieved by proposing a new technique, Optimized A* (OA*). The proposed OA* is the hybrid version of three techniques that are ABA* (Adaptive Bidirectional A*), VTM (Vector triangulation method), and PSF (Path smoothing filter which is the modification of Bezier curve technique). The conventional A* only deal with the heuristic values of concerned nodes, but the proposed ABA* algorithm also considers the occupied cells/ obstacles present near the starting and goal nodes in the grid cells to select the shortest path from start to destination. Due to the involvement of the occupied cells, the execution time, path curves, and the number of operations is primarily reduced because occupied nodes are considered as closed nodes, and its heuristic calculation is not considered in the conventional method. The trajectory generated by the proposed ABA* technique is also further made smoother by implementing a path smoothing filter that is the combination of VTM and modified Bezier curve technique based on selecting optimal control point for the collision free trajectory. While implementing the proposed OA* makes the path trajectory become smoother by reducing the number of sharp turns by 85.36% w.r.t traditional ACO, 73.91% w.r.t improved ACO, and 64.70% w.r.t hybrid solution of ACO+ A*. Due to the reduction in the number of sharp turns in the path trajectory, the acceleration of the mobile robot is increased by 52.96% w.r.t traditional ACO, 28.63% w.r.t improved ACO, and 19.96% w.r.t hybrid solution of ACO+ A*.

Quantifying predation on benthos and its overlap with bottom fishing in the NW Atlantic

AbstractContinental shelves experience many human pressures with demersal fisheries central to disturbing the ocean floor. However, ecological processes such as predation rates of benthos and their relationship with bottom fishing are often unknown for large marine ecosystems. We examined the amount of benthos consumed by 14 benthivorous fishes, the overlap between benthos predation and bottom fishing (dredge and trawl gear), and temporal trends in benthivorous fish abundance and the number of fishing trips on the northeast US continental shelf. Mean annual predation (biomass of prey removed) and 95% confidence intervals ranged from .0002 (.0001–.0003) to 3967 (1761–7112) t per 10‐min area squared grid cell and prey taxa for these benthivorous fishes. Predation and bottom fishing had overlapping footprints of activity, which were slightly stronger for dredge gear. Trophic success (ratio of prey biomass eaten to the biomass of the benthivore community per grid cell) revealed more Bivalvia, Gammaridea, and Polychaeta eaten in areas targeted by trawling with more fish biomass. In contrast, dredging did not target fish biomass, but these areas had increased (Echinoidea, Gammaridea, and other benthos) or decreased (Ophiuroidea) trophic success relative to dredging footprint, suggesting habitat preferences for benthic prey and demersal fisheries have converged rather than diverged. Despite declines in bottom fishing, recent increases in benthivorous fish abundance and growing interest in ocean floor use suggest that fisheries managers should ensure benthivores have sufficient prey resources relative to their community size and human pressures to promote long‐term sustainability of demersal fisheries and healthy ecosystems.

Distance-based indicators for evaluating environmental multi-contamination and related exposure: how far should we go?

Assessing environmental exposure to pollution is a challenging task, and scientists often use distance-based or proximity indicators when field or modeled data are unavailable. Although buffers are commonly used to represent the impact of a pollution source on neighboring populations, they can result in high-exposure misclassification. Euclidean distance-based indicators offer a promising alternative, but practices vary significantly in the literature. In this study, we aimed to compare several distance-based indicators for multiple environmental contaminants in an industrial and urban area. At the population's grid cell resolution of 200 × 200m, we compared the distance to the closest source, the average or median distance to all sources, or a restricted number of nearby sources for six types of sources (industries, railways, rail areas, roadways, road crossings, and agricultural patches) against environmental contamination data (PM10, NO2, and multimetallic contamination in lichens). Our findings revealed that the representativeness of contamination by indicators is significantly affected by the type and number of nearby sources considered. Specifically, we found that considering the distance to the nearest source or the average distance to all sources can lead to exposure misclassifications. The optimal correlation between distance indicators and pollutant levels was observed when considering 10-14 of the closest industrial sources, located within a 4.9- to 5.5-km radius. For rail areas, the optimal number was two to three sources within a 5.4- to 7.4-km radius. For main roads, intersections, and railways, the optimal number of sources varied depending on the pollutant, generally falling within a 3- to 9.4-km radius. Environmental contamination is influenced by the diversity of nearby sources, and considering only one source increases the risk of misclassification. Our results suggest that proximity models are still appropriate for study areas where the etiology of existing health effects is unclear, providing an exploratory analysis before more sophisticated research.

The Spatiotemporal Matching Relationship between Metro Networks and Urban Population from an Evolutionary Perspective: Passive Adaptation or Active Guidance?

With the operation of the first route in Xi’an City, the matching relationship between the metro networks and the urban population is a root factor affecting the utilization of rail transit facilities. The mismatch between the metro networks and the urban population has led to an imbalance between the supply and demand for rail transport, resulting in wasted urban infrastructure. Based on this issue, the research objective is to focus on the spatiotemporal variations of the matching relationship. Firstly, the topological network model abstractly extracted metro spatial distribution features, and the spatial autocorrelation model was adopted to identify the evolution characteristics of the metro networks and urban population. Secondly, this paper adopted a time-lagged regression model to demonstrate the action relationship from 2011 to 2021. Then, the compositive coordination index was utilized to assess the variation of the global matching relationship. Finally, the paper explored spatial heterogeneity through the coupling coherence degree attached to grid cells. The research results indicate that the Moran’s I value of metro elements decreased from 0.782 to 0.510 with the further complexity of topological networks, while the population was consistently high in spatial dependence with a Moran’s I value of around 0.75 during the decade. Based on the regression coefficients and significance, this paper verified the hypothesis that the metro networks and urban population had a positive time-lagged feedback effect in urban development. From 2011 to 2021, the compositive coordination index symbolizing the global matching relationship increased from 0.29 to 0.90, but the coupling coherence degree shows significant spatial heterogeneity in different grid units. Differentiated spatial planning strategies were proposed for varied areas to efficiently utilize rail transit, which may provide a reference for other cities with the same reality problem.

A Constrained Spectral Approximation of Subgrid‐Scale Orography on Unstructured Grids

AbstractThe representation of subgrid‐scale orography is a challenge in the physical parameterization of orographic gravity‐wave sources in weather forecasting. A significant hurdle is encoding as much physical information with as simple a representation as possible. Other issues include scale awareness, that is, the orographic representation has to change according to the grid cell size and usability on unstructured geodesic grids with non‐quadrilateral grid cells. This work introduces a novel spectral analysis method approximating a scale‐aware spectrum of subgrid‐scale orography on unstructured geodesic grids. The dimension of the physical orographic data is reduced by more than two orders of magnitude in its spectral representation. Simultaneously, the power of the approximated spectrum is close to the physical value. The method is based on well‐known least‐squares spectral analyses. However, it is robust to the choice of the free parameters, and tuning the algorithm is generally unnecessary. Numerical experiments involving an idealized setup show that this novel spectral analysis performs significantly better than a straightforward least‐squares spectral analysis in representing the physical energy of a spectrum. Studies involving real‐world topographic data are conducted, and reasonable error scores within ±10% error relative to the maximum physical quantity of interest are achieved across different grid sizes and background wind speeds. The deterministic behavior of the method is investigated along with its principal capabilities and potential biases, and it is shown that the error scores can be iteratively improved if an optimization target is known. Discussions on the method's limitations and broader applicability conclude this work.

Source Attribution of Health Burdens From Ambient PM2.5, O3, and NO2 Exposure for Assessment of South Korean National Emission Control Scenarios by 2050.

We quantify anthropogenic sources of health burdens associated with ambient air pollution exposure in South Korea and forecast future health burdens using domestic emission control scenarios by 2050 provided by the United Nations Environment Programme (UNEP). Our health burden estimation framework uses GEOS-Chem simulations, satellite-derived NO2, and ground-based observations of PM2.5, O3, and NO2. We estimate 19,000, 3,300, and 8,500 premature deaths owing to long-term exposure to PM2.5, O3, and NO2, respectively, and 23,000 NO2-associated childhood asthma incidences in 2016. Next, we calculate anthropogenic emission contributions to these four health burdens from each species and grid cell using adjoint sensitivity analysis. Domestic sources account for 56%, 38%, 87%, and 88% of marginal emission contributions to the PM2.5-, O3-, and NO2-associated premature deaths and the NO2-associated childhood asthma incidences, respectively. We project health burdens to 2050 using UNEP domestic emission scenarios (Baseline and Mitigation) and population forecasts from Statistics Korea. Because of population aging alone, there are 41,000, 10,000, and 20,000 more premature deaths associated with PM2.5, O3, and NO2 exposure, respectively, and 9,000 fewer childhood asthma incidences associated with NO2. The Mitigation scenario doubles the NO2-associated health benefits over the Baseline scenario, preventing 24,000 premature deaths and 13,000 childhood asthma incidences by 2050. It also slightly reduces PM2.5- and O3-associated premature deaths by 9.9% and 7.0%, unlike the Baseline scenario where these pollutants increase. Furthermore, we examine foreign emission impacts from nine SSP/RCP-based scenarios, highlighting the need for international cooperation to reduce PM2.5 and O3 pollution.

Determinantal point process attention over grid cell code supports out of distribution generalization.

Deep neural networks have made tremendous gains in emulating human-like intelligence, and have been used increasingly as ways of understanding how the brain may solve the complex computational problems on which this relies. However, these still fall short of, and therefore fail to provide insight into how the brain supports strong forms of generalization of which humans are capable. One such case is out-of-distribution (OOD) generalization - successful performance on test examples that lie outside the distribution of the training set. Here, we identify properties of processing in the brain that may contribute to this ability. We describe a two-part algorithm that draws on specific features of neural computation to achieve OOD generalization, and provide a proof of concept by evaluating performance on two challenging cognitive tasks. First we draw on the fact that the mammalian brain represents metric spaces using grid cell code (e.g., in the entorhinal cortex): abstract representations of relational structure, organized in recurring motifs that cover the representational space. Second, we propose an attentional mechanism that operates over the grid cell code using determinantal point process (DPP), that we call DPP attention (DPP-A) - a transformation that ensures maximum sparseness in the coverage of that space. We show that a loss function that combines standard task-optimized error with DPP-A can exploit the recurring motifs in the grid cell code, and can be integrated with common architectures to achieve strong OOD generalization performance on analogy and arithmetic tasks. This provides both an interpretation of how the grid cell code in the mammalian brain may contribute to generalization performance, and at the same time a potential means for improving such capabilities in artificial neural networks.

Evaluation of IMERG and MSG-CPP Precipitation Estimates over Europe Using EURADCLIM: A Gauge-Adjusted European Composite Radar Dataset

Abstract A new pan-European climatological dataset was recently released that has a much higher spatiotemporal resolution than existing pan-European interpolated rain gauge datasets. This radar dataset of hourly precipitation accumulations, European Radar Climatology (EURADCLIM) (Overeem et al.), covers most of continental Europe with a resolution of 2 km × 2 km and is adjusted employing data from potentially thousands of government rain gauges. This study aims to use this dataset to evaluate two important satellite-derived precipitation products over the period 2013–19 at a much higher spatiotemporal resolution than was previously possible at the European scale: the IMERG late run and the Meteosat Second Generation (MSG) cloud physical property product from the SEVIRI instrument. The latter is only available during daytime, so the analyses are restricted to daytime conditions. A direct gridcell comparison of hourly precipitation reveals an apparently low coefficient of correlation. However, looking into slightly more detail at statistics pertaining to longer time scales or specific areas, the datasets show good correspondence. All datasets are shown to have their specific biases, which can be transient or more systematic, depending on the timing or location. The MSG precipitation seems to have an overall positive bias, and the IMERG dataset suffers from some transient overestimation of certain events.

Streamflow Intermittence in Europe: Estimating High‐Resolution Monthly Time Series by Downscaling of Simulated Runoff and Random Forest Modeling

AbstractKnowing where and when rivers cease to flow provides an important basis for evaluating riverine biodiversity, biogeochemistry and ecosystem services. We present a novel modeling approach to estimate monthly time series of streamflow intermittence at high spatial resolution at the continental scale. Streamflow intermittence is quantified at more than 1.5 million river reaches in Europe as the number of no‐flow days grouped into five classes (0, 1–5, 6–15, 16–29, 30–31 no‐flow days) for each month from 1981 to 2019. Daily time series of observed streamflow at 3706 gauging stations were used to train and validate a two‐step random forest modeling approach. Important predictors were derived from time series of monthly streamflow at 73 million 15 arc‐sec (∼500 m) grid cells that were computed by downscaling the 0.5 arc‐deg (∼55 km) output of the global hydrological model WaterGAP, which accounts for human water use. Of the observed perennial and non‐perennial station‐months, 97.8% and 86.4%, respectively, were correctly predicted. Interannual variations of the number of non‐perennial months at non‐perennial reaches were satisfactorily simulated, with a median Pearson correlation of 0.5. While the spatial prevalence of non‐perennial reaches is underestimated, the number of non‐perennial months is overestimated in dry regions of Europe where artificial storage abounds. Our model estimates that 3.8% of all European reach‐months and 17.2% of all reaches were non‐perennial during 1981–2019, predominantly with 30–31 no‐flow days. Although estimation uncertainty is high, our study provides, for the first time, information on the continent‐wide dynamics of non‐perennial rivers and streams.

Development and evaluation of a machine vision and deep learning-based smart sprayer system for site-specific weed management in row crops: An edge computing approach

Traditional weed management often involves blanket herbicide spraying, resulting in substantial herbicide wastage, environmental concerns, and herbicide resistant issues. Smart spraying systems utilizing robotics and sensors technologies can minimize herbicide usage and provide a sustainable solution for site-specific weed management. A machine vision-based spraying system was designed and developed for weed identification and precise spray application onto the target weeds. The sprayer platform utilizes a deep learning YOLOv4 model to accurately recognize multiple weed species, facilitating targeted spray application. The platform is equipped with an FLIR RGB camera for real-time image acquisition and Nvidia Jetson AGX Orin edge device for deploying weed detection deep-learning model. The GPIO pins of Nvidia Jetson were utilized to activate relay, providing precise on/off control over the TeeJet solenoid valves for spot spraying. Both indoor and field experiments were conducted to evaluate and compare the performance of vision-based sprayer system for weed identification and precise spraying onto the target weeds. In the indoor experiment, the sprayer system showed the average effective spraying rate of 93.33 %, with the precision of 100 % and recall of 92.8 %. Conversely, the field experiment resulted in a slightly lower average effective spraying rate of 90.6 %, while maintaining a precision of 95.5 % and a recall of 89.47 %. The reduced accuracy of the spraying system in field experiment was due to varying outdoor conditions such as lighting, shadows, and wind velocity. Overall, the result of this study demonstrates the spraying system's potential for targeted herbicide application onto the grid cells containing weeds, effectively reducing herbicide usage and overall weed management costs.

Assessing coastal population capacity in Tsunami-prone areas: A grid-based approach.

The research results show that using grid cells to analyse areas affected by the tsunami can provide excellent and informative results. Research findings at the research location regarding community preparedness in facing tsunamis show that communities at risk of being affected by the tsunami need to increase their capacity because the majority of communities in coastal areas, especially in the Sidorejo sub-village, have been identified as having low capacity according to several livelihood asset parameters such as financial capital in income. By increasing individual capacity, it is hoped that society will be able to avoid the threat of tsunami waves better.

Impacts of the Chengdu 2021 world university games on NO2 pollution: Implications for urban vehicle electrification promotion

Emissions of nitrogen oxides (NOx) are a dominant contributor to ambient nitrogen dioxide (NO2) concentrations, but the quantitative relationship between them at an intracity scale remains elusive. The Chengdu 2021 FISU World University Games (July 22 to August 10, 2023) was the first world-class multisport event in China after the COVID-19 pandemic which led to a substantial decline in NOx emissions in Chengdu. This study evaluated the impact of variations in NOx emissions on NO2 concentrations at a fine spatiotemporal scale by leveraging this event-driven experiment. Based on ground-based and satellite observations, we developed a data-driven approach to estimate full-coverage hourly NO2 concentrations at 1 km resolution. Then, a random-forest-based meteorological normalization method was applied to decouple the impact of meteorological conditions on NO2 concentrations for every grid cell, the resulting data were then compared with the timely bottom-up NOx emissions. The SHapley-Additive-exPlanation (SHAP) method was employed to delineate the individual contributions of meteorological factors and various emission sources to the changes in NO2 concentrations. According to the full-coverage meteorologically normalized NO2 concentrations, a decrease in NOx emissions and favorable meteorological conditions accounted for 80 % and 20 % of the NO2 reduction, respectively, across Chengdu city during the control period. Within the strict control zone, a 30 % decrease in the meteorologically normalized NO2 concentrations was observed during the control period. The normalized NO2 concentrations demonstrated a strong correlation with NOx emissions (R = 0.96). Based on the SHAP analysis, traffic emissions accounted for 73 % of the reduction in NO2 concentrations, underscoring the significance of traffic control measures in improving air quality in urban areas. This study provides insights into the relationship between NO2 concentrations and NOx emissions using real-world data, which implies the substantial benefits of vehicle electrification for sustainable urban development.

Walking fingerprinting

Abstract We consider the problem of predicting an individual’s identity from accelerometry data collected during walking. In a previous paper, we transformed the accelerometry time series into an image by constructing the joint distribution of the acceleration and lagged acceleration for a vector of lags. Predictors derived by partitioning this image into grid cells were used in logistic regression to predict individuals. Here, we (a) implement machine learning methods for prediction using the grid cell-derived predictors; (b) derive inferential methods to screen for the most predictive grid cells while adjusting for correlation and multiple comparisons; and (c) develop a novel multivariate functional regression model that avoids partitioning the predictor space. Prediction methods are compared on two open source acceleometry data sets collected from: (a) 32 individuals walking on a 1.06 km path; and (b) six repetitions of walking on a 20 m path on two occasions at least 1 week apart for 153 study participants. In the 32-individual study, all methods achieve at least 95% rank-1 accuracy, while in the 153-individual study, accuracy varies from 41% to 98%, depending on the method and prediction task. Methods provide insights into why some individuals are easier to predict than others.

Mapping terrestrial mammals in China based on species richness of functional groups

Biodiversity loss is one of the most critical environmental issues globally. Functional diversity and functional groups, extending and expanding the concept of species richness, provide a more comprehensive understanding of biodiversity. By integrating mathematical indices calculation with classical geospatial statistical analysis and mapping techniques, we explored and visually illustrated the spatial patterns and regularities of functional diversity. This approach shed light on the functional diversity of China’s terrestrial mammals from a macroecological perspective. We found that in terms of overall quantity composition, underground-living mammals (UG groups) had the highest proportion (55.1%) in the vertical niche dimension, while nocturnal mammals (NO groups) dominated (58.6%) the in temporal niche dimension. Employing grid cells as fundamental units, in most grid cells, the proportion of UG and NO groups exceeded 50%, consistent with the overall quantity results. Examining the spatial distribution of functional group species richness from different perspectives reveals varying patterns. For instance, standard deviations of vertical and temporal niches showed different spatial clustering traits on either side of the Hu Huanyong Line. In a specific dimension of functional diversity, the numerical differences among functional groups are larger in grasslands with homogenous vegetation, while in forests with diverse vegetation types, these differences are minimized. Additionally, if there is a large disparity in species richness among functional groups in one dimension, there may be a smaller disparity in another. These findings offer a macroecological perspective for biodiversity research, providing insights for interdisciplinary studies in basic ecology, conservation biology, and geography, and offering scientific guidance for mammal conservation.

Adopting a cell-based method to identify mineralized geological structures in support of mineral prospectivity mapping at the Kuh–Lakht epithermal gold deposit, Central Iran

Mineral prospectivity mapping (MPM) is a new tool in mineral exploration with the objective of gathering and integrating multi-source evidence to provide decision-making criteria for exploration targeting. Ideally, the MPM approach accounts for the uncertainties associated with different predictive modeling techniques. The cell-based association (CBA) is a computational workflow that addresses the prediction-related bias of the MPM protocols. In this contribution, the CBA technique was employed to prioritize grid cells according to the importance of mineralized geological features and to identify geological settings favorable for ore deposition. The Kuh-Lakht epithermal gold deposit in Central Iran was adopted as a case study to examine the relevance of the proposed workflow and define target areas for brownfield exploration. To assess the significance of the proposed technique in dealing with evidence layers rather than training locations (i.e., mineralized/non-mineralized sites), two geochemical evidence layers, single-element and multi-element signatures, were used to train the CBA technique. An appraisal analysis through prediction-area plots and success-rate curves indicated that multi-element geochemical signatures are more relevant for delineating mineralized geological structures and vectoring for concealed gold mineralization. Notably, the input of single-element geochemical data into the CBA workflow enabled the prediction of additional exploration targets, whereas multi-element geochemical data failed to achieve the same results. The results indicated that the CBA technique can efficiently cope with the uncertainty of insufficient data input and permit the delineation of exploration targets. Additionally, our results suggest that the incorporation of a ranking system of critical geological factors that facilitate the gold mineralization into conventional MPM protocols can improve the robustness of mineral exploration targeting.

An open data-based model for generating a synthetic low-voltage grid to estimate hosting capacity

This study develops and applies an open data-based reference electricity grid analysis (REGAL) model designed to create a synthetic representation of a low-voltage (LV) grid for a country-size geographic area. The model enables large-scale grid simulation in which new loads, such as electric vehicle charging, can be added to estimate their impacts on the current LV grid. The modeling is carried out in three steps: (1) generation of a synthetic LV grid; (2) addition of residential loads, including electric vehicle charging; and (3) evaluating if the grid capacity is exceeded. The grid is generated by selecting transformers and cables so that the system can fulfill the current demand while meeting national regulations and standards for distribution grids, all at the lowest total cost. This paper presents the results of calibration and validation against real-world data for the predicted electricity demands and synthetic grid generated by the model. Different calibration values were explored, and the accuracy of the estimations of grid capacities was calibrated using proprietary real-world data from grid operators. For a region with multiple grid cells, an average deviation from real-world data of ±10 % was achieved. For an average area of 1 km2, the error was 44.5 %, which means that the model is not suitable for analysis on this geographic level. However, the level of accuracy is deemed sufficient for initial estimations of hosting capacity for larger geographic areas, such as a region or a country, thereby enabling estimations of hosting capacity in new areas that lack publicly accessible grid capacities.

Optimisation of Bridge Pier Winding Flow Numerical Simulation Scheme Based on Delft3D

The majority of existing numerical simulations of the effect of bridge piers on water’s movement are based on a limited number of bridge piers at a laboratory scale. Furthermore, some 2D numerical simulations for actual bridge projects have deficiencies, including the use of overly large meshes and an inadequate treatment of bridge piers. In this study, we compare three methods (structural mesh encryption, suspension mesh, and non-structural mesh) based on Delft3D, and we apply the optimisation scheme to a real bridge project. It is demonstrated that optimal results can be achieved by utilising a grid size comparable to the pier diameter (Dp) in the region away from the pier. In the vicinity of the pier, the grid cell size should be no larger than 1/9 Dp. The suspended grid technique (DD Boundary) can yield results consistent with those obtained using a full-area high-resolution grid, provided that the total grid number can be reduced and the computational time is considerably reduced. In this study, the unstructured mesh (Delft3D Flexible Mesh) scheme was unable to capture the oscillations in the wake flow behind the bridge piers. However, the application of the optimised scheme in bridge engineering demonstrated its practical value. The findings of this study on mesh resolution and suspension mesh schemes can be applied to the Delft3D software and are also useful for other numerical simulation work.

KBAscope: key biodiversity area identification in R

Key Biodiversity Areas (KBAs) represent the largest global network of sites critical to the persistence of biodiversity, which have been identified against standardised quantitative criteria. Sites that hold very high biodiversity value or potential are given specific attention on site‐based conservation targets of the Kunming‐Montreal Global Biodiversity Framework (GBF), and KBAs are already used in indicators for the GBF and the Sustainable Development Goals. However, most of the species that trigger KBA status are birds and to maximise benefits for biodiversity under the actions taken to fulfil the GBF, countries need to update their KBAs to represent important sites across multiple taxa. Here we introduce KBAscope, an R package to identify potential KBAs using multiple taxonomic groups. KBAscope provides flexible, user‐friendly functions to edit species data (population, range maps, area of occupancy, area of habitat and localities); apply KBA criteria; and generate outputs to support the delineation and validation of KBAs. The details of the analysis – such as the spatial units tested or the KBA criteria applied – can be decided according to the scope of the analysis. We demonstrate the functionality of KBAscope by using it to identify potential KBAs in Greece based on multiple terrestrial taxonomic groups and four sizes of grid cells (4 km2, 25 km2, 100 km2, 225 km2).

The Impact of Foreign Aid on Local Development: A Grid Cell Analysis

We examine whether geocoded World Bank foreign aid projects affect economic activity at the level of equally sized grid cells with a spatial resolution of 0.5 decimal degrees latitude and longitude. We use geospatial data for 58,676 grid cells during the period 1997-2013 with about one million grid-by-year observations. Using fixed effects estimating techniques, we evaluate the impact of World Bank foreign aid projects on grid cell night-time light growth. The estimates reveal that World Bank foreign aid projects contribute significantly and positively to grid cell economic activity measured by night-time light growth. We complement the fast-growing literature using geocoded data to assess aid effectiveness at the subnational level. We provide evidence that the lack of robust empirical findings at the country and subnational (e.g., ADM1 and ADM2) level can be explained due to spatial aggregation and the choice of spatial units. Additional estimates and robustness tests further corroborate our main findings.