Spatial Covariates Research Articles

Mapping refugee populations at high resolution by unlocking humanitarian administrative data

BackgroundInforming local decision-making, improving service delivery and designing household surveys require having access to high-spatial resolution mapping of the targeted population. However, this detailed spatial information remains unavailable for specific population subgroups, such as refugees, a vulnerable group that would significantly benefit from focused interventions. Given the continuous increase in the number of refugees, reaching an all-time high of 35.3 million people in 2022, it is imperative to develop models that can accurately inform about their spatial locations, enabling better and more tailored assistance.MethodsWe leverage routinely collected registration data on refugees and combine it with high-resolution population maps, satellite imagery derived settlement maps and other spatial covariates to disaggregate observed refugee totals into 100-m grid cells. We suggest a deterministic grid cell allocation inside monitored refugee sites based on building count and a random-forest-derived grid cell allocation outside refugee sites based on geolocating the textual geographic information in the refugee register and on high-resolution population mapping. We test the method in Cameroon using the registration database monitored by the United Nations High Commissioner for Refugees.ResultsUsing OpenStreetMap, 83% of the manually inputted information in the registration database could be geolocated. The building footprint layer derived from satellite imagery by Ecopia AI offers extensive coverage within monitored refugee sites, although manual digitization was still required in rapidly evolving settings. The high-resolution mapping of refugees on a 100-m grid basis provides an unparalleled level of spatial detail, enabling valuable geospatial insights for informed local decision-making.ConclusionsGathering information on forcibly displaced persons in sparse data-setting environment can quickly become very costly. Therefore, it is critical to gain the most knowledge from operational data that is frequently collected, such as registration databases. Integrating it with ancillary information derived from satellite imagery paves the way for obtaining more timely and spatially precise information to better deliver services and enhance sampling frame for target data collection exercises that further improves the quality of information on people in need.

Bayesian estimation of spatially varying mortality risk using tagged animal data

Abstract The survival of animals is impacted by landscapes of spatially varying mortality factors including habitat type, predation risk or harvest risk, among others. Characterization of these spatial mortality processes is important for managing animal populations and their habitats, yet this information has proved challenging to capture. Advances in telemetry now make it possible to monitor tagged individuals' locations through time for a wide range of taxa, providing opportunity to assess movement and mortality simultaneously through spatial time‐to‐event data. Existing time‐to‐event modelling frameworks have largely ignored spatially varying mortality processes or have treated an animal's location as fixed at a regional level. Here we present a general spatial survival model for analysing time‐to‐event data arising from animal telemetry. Our model has a flexible Cox regression form and can estimate the effects of conventional non‐spatial risk factors (size, seasonality, etc.), spatial covariates (e.g. habitat type) and spatial variation in risk not explained by covariates on mortality. We show how to fit our model using Bayesian methods and demonstrate applications of our model with several simulated examples and two animal telemetry case studies. Our model produced consistent and unbiased parameter estimates throughout simulations with a variety of spatial and non‐spatial hazards. In the first case study, data from 147 tagged caribou in British Columbia, Canada, revealed a spatially heterogeneous mortality landscape with caribou survival varying by elevation, likely in response to space use by predators. Our second case study involved a dataset of 390 acoustically tagged Atlantic cod in a southern Norwegian fjord where a marine protected area (MPA) was established during the study. We found that the MPA led to a shift from mostly fishing mortality to mostly natural mortality within the fjord and that these risks had markedly different spatial hazard patterns. Our spatial time‐to‐event model can make use of data from a variety of widely used telemetry technologies to characterize landscapes of mortality risks for different taxa. Our work provides new opportunities to inform the spatial ecology and management of fish and wildlife populations.

Spatial patterns and associated factors of HIV testing and counselling (HTC) as a component of antenatal care services in Ethiopia

Background While HIV testing and counselling play a crucial role in preventing mother-to-child transmission, numerous pregnant women did not receive these services. Understanding the spatial variation of HIV testing and counselling and its associated factors during antenatal care in Ethiopia remains limited. Thus, this study was aimed at assessing the spatial patterns and factors associated with HIV testing and counselling during antenatal care visits in Ethiopia. Methods A cross-sectional study design was employed with a two-stage stratified cluster sampling technique. A total of 2,789 women who gave birth in the two years prior to the survey and had at least one antenatal care visit were included in the study. Stata version 16 and ArcGIS version 10.8 software were used for analysis. A multilevel robust Poisson regression model was fitted to identify significantly associated factors since the prevalence of HIV testing and counselling was higher than 10%. A statistically significant association was declared based on multivariable multilevel robust Poisson regression analysis using an adjusted prevalence ratio with its 95% confidence interval at a p-value < 0.05. Spatial regression analysis was conducted, and the local coefficients of statistically significant spatial covariates were visualised. Results In Ethiopia, the overall prevalence of HIV testing and counselling during antenatal care visits was 29.5% (95% CI: 27.8%, 31.2%). Significant spatial clustering was observed (Global Moran’s I = 0.138, p-value <0.001). In the spatial regression analysis, high and comprehensive knowledge related to HIV, and comprehensive knowledge on the prevention of mother-to-child transmission were significant explanatory variables for the spatial variation of HIV testing and counselling. In the multivariable multilevel robust Poisson regression analysis, education, household wealth, media exposure, number of antenatal care visits, comprehensive knowledge on mother-to-child transmission, comprehensive knowledge on prevention of mother-to-child transmission, and region were significantly associated factors. Conclusion The prevalence of HIV testing and counselling during antenatal care visits was low. Empowering women through education, promoting mass media exposure, increasing numbers of antenatal care visits, and enhancing women’s knowledge related to HIV and mother-to-child transmission by targeting cold spot areas could improve HIV testing and counselling service uptake among pregnant women in Ethiopia.

Enhancing Remote Sensing Water Quality Inversion through Integration of Multisource Spatial Covariates: A Case Study of Hong Kong’s Coastal Nutrient Concentrations

The application of remote sensing technology for water quality monitoring has attracted much attention recently. Remote sensing inversion in coastal waters with complex hydrodynamics for non-optically active parameters such as total nitrogen (TN) and total phosphorus (TP) remains a challenge. Existing studies build the relationships between remote sensing spectral data and TN/TP directly or indirectly via the mediation of optically active parameters (e.g., total suspended solids). Such models are often prone to overfitting, performing well with the training set but underperforming with the testing set, even though both datasets are from the same region. Using the Hong Kong coastal region as a case study, we address this issue by incorporating spatial covariates such as hydrometeorological and locational variables as additional input features for machine learning-based inversion models. The proposed model effectively alleviates overfitting while maintaining a decent level of accuracy (R2 exceeding 0.7) during the training, validation and testing steps. The gap between model R2 values in training and testing sets is controlled within 7%. A bootstrap uncertainty analysis shows significantly improved model performance as compared to the model with only remote sensing inputs. We further employ the Shapely Additive Explanations (SHAP) analysis to explore each input’s contribution to the model prediction, verifying the important role of hydrometeorological and locational variables. Our results provide a new perspective for the development of remote sensing inversion models for TN and TP in similar coastal waters.

Biotic and abiotic drivers of harbor seal (Phoca vitulina) fine‐scale densities in the Salish Sea

AbstractUnderstanding relationships between environmental characteristics and variation in species occurrence and density can provide information for managing human activities, protected species, and species of commercial importance in a dynamic system. To identify environmental drivers associated with variation in harbor seal (Phoca vitulina) densities in the Salish Sea, Washington, we analyzed 20 years of boat‐based survey data and environmental covariates using a hierarchical distance sampling model. We included spatial, temporal, and spatiotemporal environmental covariates in our model and produced fine‐scale predictive maps displaying in‐water estimated densities from our model results. We found that spatial covariates were the strongest predictors for harbor seal densities in the Salish Sea. Harbor seals were more abundant closer to major river mouths, near shore, in shallower waters, and in areas with more haul‐out sites. Additionally, harbor seal density varied with shoreline type. Changes in predicted harbor seal spatial use of the Salish Sea varied but with little difference between breeding/molting and nonbreeding/nonmolting seasons. Our results revealed spatiotemporal variation in harbor seal fine‐scale density in the Salish Sea, which are particularly important for conservation planning, as spatiotemporal variation in harbor seal density can exert heterogenous top‐down effects on prey species populations, some of which are threatened.

EM algorithm for generalized Ridge regression with spatial covariates

AbstractThe generalized Ridge penalty is a powerful tool for dealing with multicollinearity and high‐dimensionality in regression problems. The generalized Ridge regression can be derived as the mean of a posterior distribution with a Normal prior and a given covariance matrix. The covariance matrix controls the structure of the coefficients, which depends on the particular application. For example, it is appropriate to assume that the coefficients have a spatial structure when the covariates are spatially correlated. This study proposes an Expectation‐Maximization algorithm for estimating generalized Ridge parameters whose covariance structure depends on specific parameters. We focus on three cases: diagonal (when the covariance matrix is diagonal with constant elements), Matérn, and conditional autoregressive covariances. A simulation study is conducted to evaluate the performance of the proposed method, and then the method is applied to predict ocean wave heights using wind conditions.

Watershed features shape spatial patterns of fish tissue mercury in a boreal river network

Freshwater mercury (Hg) contamination is a widespread environmental concern but how proximate sources and downstream transport shape Hg spatial patterns in riverine food webs is poorly understood. We measured total Hg (THg) in slimy sculpin (Cottus cognatus) across the Kuskokwim River, a large boreal river in western Alaska and home to subsistence fishing communities which rely on fish for primary nutrition. We used spatial stream network models (SSNMs) to quantify watershed and instream conditions influencing sculpin THg. Spatial covariates for local watershed geology and slope accounted for 55 % of observed variation in sculpin THg and evidence for downstream transport of Hg in sculpins was weak. Empirical semivariograms indicated these spatial covariates accounted for most spatial autocorrelation in observed THg. Watershed geology and slope explained up to 70 % of sculpin THg variation when SSNMs accounted for instream spatial dependence. Our results provide network-wide predictions for fish tissue THg based largely on publicly available geospatial data and open-source software for SSNMs, and demonstrate how these emerging models can be used to understand contaminant behavior in spatially complex aquatic ecosystems.

Nonparametric Testing of the Covariate Significance for Spatial Point Patterns under the Presence of Nuisance Covariates

Determining the relevant spatial covariates is one of the most important problems in the analysis of point patterns. Parametric methods may lead to incorrect conclusions, especially when the model of interactions between points is wrong. Therefore, we propose a fully nonparametric approach to testing significance of a covariate, taking into account the possible effects of nuisance covariates. Our tests match the nominal significance level, and their powers are comparable with the powers of parametric tests in cases where both the model for intensity function and the model for interactions are correct. When the parametric model for the intensity function is wrong, our tests achieve higher powers. The proposed methods rely on Monte Carlo testing and take advantage of the newly introduced concepts: the covariate-weighted residual measure and nonparametric residuals. We also define a correlation coefficient between a point process and a covariate and a partial correlation coefficient quantifying the dependence between a point process and a covariate of interest while removing the influence of nuisance covariates. Supplementary materials for this article are available online.

Influence of resource gradients and habitat edges on density variation in tiger populations in a multi-use landscape

Spatial heterogeneity in the local densities of terrestrial carnivores is driven by multiple interacting biotic and abiotic factors. Space-use patterns of large carnivores reflect the competing demands of resource selection (e.g., exploitation of habitats with abundant prey) and minimization of risks arising from human interactions. Estimating the relative strength of these drivers is essential to understand spatial variation in densities of large carnivores and there are still key knowledge gaps for many large carnivore populations. To better understand the relative roles of environmental and human drivers of spatial variation in tiger (Panthera tigris) densities, we surveyed a 3000 km2 landscape in North India using camera trap data. Over two years, we photo-captured 92 unique adult tigers. Associating spatial covariates with patterns of detection allowed us to test hypotheses about the relative influence of prey abundance, habitat structure and extent, and proximity to habitat edges on spatial variation in tiger densities across a gradient of anthropogenic disturbance. We documented extensive variation in tiger density within and across management units and protected areas. Spatial variation in prey abundance and proximity to grassland habitats, rather than human use (e.g. extent of human-dominated edge habitat and protection status), explained most of the spatial variation in tiger density in two of the five surveyed sites. The region’s largest tiger population occurred in a multi-use forest beyond protected area boundaries, where wild ungulates were abundant. Our results suggest that tigers can occur at high densities in areas with extensive human use, provided sufficiently high prey densities, and tracts of refuge habitats (eg. areas with dense vegetation with low human use). We argue that tiger conservation portfolio can be expanded across multi-use landscapes with a focus on areas that are adaptively managed as “zones of coexistence” and “refuge habitats”. Advancing this conservation strategy is contingent on greatly strengthening systems to effectively and equitably redress human–wildlife conflict and leveraging existing policies to strengthen local participation in conservation planning and forest stewardship. Our insights into the environmental drivers of spatial heterogeneity in tiger populations can inform both local management and guide to species recovery in working landscapes.

Updated range map of an endangered salamander and congeneric competitor reveals different niche preferences.

Estimating distributions for cryptic and highly range-restricted species induces unique challenges for species distribution modeling. In particular, bioclimatic covariates that are typically used to model species ranges at regional and continental scales may not show strong variation at scales of 100s and 10s of meters. This limits both the likelihood and usefulness of correlated occurrence to data typically used in distribution models. Here, we present analyses of species distributions, at 100 × 100 m resolution, for a highly range restricted salamander species (Shenandoah salamander, Plethodon shenandoah) and a closely related congener (red-backed salamander, Plethodon cinereus). We combined data across multiple survey types, account for seasonal variation in availability of our target species, and control for repeated surveys at locations- all typical challenges in range-scale monitoring datasets. We fit distribution models using generalized additive models that account for spatial covariates as well as unexplained spatial variation and spatial uncertainty. Our model accommodates different survey protocols using offsets and incorporates temporal variation in detection and availability resulting from survey-specific variation in temperature and precipitation. Our spatial random effect was crucial in identifying small-scale differences in the occurrence of each species and provides cell-specific estimates of uncertainty in the density of salamanders across the range. Counts of both species were seen to increase in the 3 days following a precipitation event. However, P. cinereus were observed even in extremely wet conditions, while surface activity of P. shenandoah was associated with a more narrow range. Our results demonstrate how a flexible analytical approach improves estimates of both distribution and uncertainty, and identify key abiotic relationships, even at small spatial scales and when scales of empirical data are mismatched. While our approach is especially valuable for species with small ranges, controlling for spatial autocorrelation, estimating spatial uncertainty, and incorporating survey-specific information in estimates can improve the reliability of distribution models in general.

North Atlantic right whale density surface model for the US Atlantic evaluated with passive acoustic monitoring

The Critically Endangered North Atlantic right whale Eubalaena glacialis entered a population decline around 2011. To save this species without closing the ocean to human activities requires detailed information about its intra-annual density patterns that can be used to assess and mitigate human-caused risks. Using 2.9 million km of visual line-transect survey effort from the US Atlantic and Canadian Maritimes conducted in 2003-2020 by 11 institutions, we modeled the absolute density (ind. km-2) of the species using spatial, temporal, and environmental covariates at a monthly time step. We accounted for detectability differences between survey platforms, teams, and conditions, and corrected all data for perception and availability biases, accounting for platform differences, whale dive behavior, group composition, and group size. We produced maps of predicted density and evaluated our results using independently collected passive acoustic monitoring (PAM) data. Densities correlated positively (r = 0.46, ρ = 0.58, τ = 0.46) with acoustic detection rates obtained at 492 stationary PAM recorders deployed across the study area (mean recorder duration = 138 d). This is the first study to quantify the concurrence of visual and acoustic observations of the species in US waters. We summarized predictions into mean monthly density and uncertainty maps for the 2003-2009 and 2010-2020 eras, based on the significant changes in the species’ spatial distribution that began around 2010. The results quantify the striking distribution shifts and provide effort- and bias-corrected density surfaces to inform risk assessments, estimations of take, and marine spatial planning.

Anthropogenic and climatic drivers of population densities in an African savanna ungulate community

AbstractMany ungulate species in Africa range in habitats that vary in type and quality over space and time, but ongoing environmental change is substantially altering their habitats. Identifying key environmental variables that regulate ungulate population densities can guide management actions for effective conservation. We studied the local population density responses of a community of sympatric ungulate species in the Tarangire Ecosystem of northern Tanzania, to a suite of environmental factors that vary over space and time, to quantify population trends, determine the primary environmental correlates of densities, and identify covariation in densities among species. We estimated seasonal densities of five commonly detected species (impala, dik–dik, Grant's gazelle, eland, and waterbuck) based on 7 years of distance‐sampling data from 41 replicate surveys of 237 line transects. We systematically analyzed the effects of spatial, seasonal, and annual environmental covariates on variation in transect species‐specific densities across space and time. Large fluctuations in climatic factors mediated highly synchronous temporal density variation among all species. We documented more spatial than temporal variation in four of the five species, suggesting that spatial heterogeneity may provide some buffer against temporal variation in the environment. Protection of sufficient habitats and water sources should allow ungulates to respond to a temporally changing world by moving across space. Further, among‐species covariation patterns identified two potential ungulate guilds (impala—dik–dik—waterbuck; eland—Grant's gazelle) that should aid in developing efficient and coordinated management actions.

Land use drives differential resource selection by African elephants in the Greater Mara Ecosystem, Kenya

Understanding drivers of space use by African elephants is critical to their conservation and management, particularly given their large home-ranges, extensive resource requirements, ecological role as ecosystem engineers, involvement in human-elephant conflict and as a target species for ivory poaching. In this study we investigated resource selection by elephants inhabiting the Greater Mara Ecosystem in Southwestern Kenya in relation to three distinct but spatially contiguous management zones: (i) the government protected Maasai Mara National Reserve (ii) community-owned wildlife conservancies, and (iii) elephant range outside any formal wildlife protected area. We combined GPS tracking data from 49 elephants with spatial covariate information to compare elephant selection across these management zones using a hierarchical Bayesian framework, providing insight regarding how human activities structure elephant spatial behavior. We also contrasted differences in selection by zone across several data strata: sex, season and time-of-day. Our results showed that the strongest selection by elephants was for closed-canopy forest and the strongest avoidance was for open-cover, but that selection behavior varied significantly by management zone and selection for cover was accentuated in human-dominated areas. When contrasting selection parameters according to strata, variability in selection parameter values reduced along a protection gradient whereby elephants tended to behave more similarly (limited plasticity) in the human dominated, unprotected zone and more variably (greater plasticity) in the protected reserve. However, avoidance of slope was consistent across all zones. Differences in selection behavior was greatest between sexes, followed by time-of-day, then management zone and finally season (where seasonal selection showed the least differentiation of the contrasts assessed). By contrasting selection coefficients across strata, our analysis quantifies behavioural switching related to human presence and impact displayed by a cognitively advanced megaherbivore. Our study broadens the knowledge base about the movement ecology of African elephants and builds our capacity for both management and conservation.

Multivariate Hawkes processes with spatial covariates for spatiotemporal event data analysis

Multivariate Hawkes processes with spatial covariates for spatiotemporal event data analysis

Marginal Distribution Fitting Method for Modelling Flood Extremes on a River Network

This study utilized a max-stable process (MSP) model with a dependence structure defined via a non-Euclidean distance metric, with the goal of modelling extreme flood data on a river network. The dataset was composed of mean daily discharge observations from 22 United States Geological Survey streamflow gaging stations for river basins in Missouri and Arkansas. The analysis included the application of the elastic-net penalty to automatically build spatially varying trend surfaces to model the marginal distributions. The dependence model accounted for the river distance between hydrologically connected gaging sites and the hydrologic distance, defined as the Euclidean distance between the centers of site’s associated drainage areas, for all stations. Modelling the marginal distributions and spatial dependence among the extremes are two key components for spatially modelling extremes. Among the 16 covariates evaluated for marginal fitting, 7 were selected to spatially model the generalized extreme value (GEV) location parameter (for each gaging station’s contributing drainage basin, its outlet elevation, centroid x coordinate, centroid elevation, area, average basin width, elevation range, and median land surface slope). The three covariates selected for the GEV scale parameter included the area, average basin width, and median land surface slope. The GEV shape parameter was assumed to be constant throughout the entire study area. Comparisons of estimates obtained from the spatial covariate model with their corresponding “at-site” estimates resulted in computed values of 0.95, 0.95, 0.94 and 0.85, 0.84, 0.90 for the coefficient of determination, Nash–Sutcliffe efficiency, and Kling–Gupta efficiency for the GEV location and scale parameters, respectively. Brown–Resnick MSP models were fit to independent multivariate events extracted from a set of common discharge data, transformed to unit Fréchet margins while considering different permutations of the non-Euclidean dependence model. Each of the fitted model’s log-likelihood values indicated improved fits when using hydrologic distance rather than Euclidean distance. They also demonstrated that accounting for flow-connected dependence and anisotropy further improved model fit. In this study, the results from both parts were illustrative; however, further research with larger datasets and more heterogeneous systems is recommended.

Assessing performance of spatial statistical stream network models in predicting alkalinity: A comparative study of two river basins in Türkiye

Developing and implementing water quality models that utilize sophisticated processes and plenty data in large river basins can be a challenging task. Factors such as sampling design, prediction at unsampled sites, and derivation of new spatial information in monitoring studies can restrict the methodologies applicable to water quality modeling. In recent years, spatial stream network (SSN) models, incorporating hydrologic distance and topological data structures, have emerged as a novel approach for predicting water quality in extensive study areas. These models have integrated conventional geostatistical methodologies and have demonstrated their effectiveness in analyzing water quality within large river basins. In this study, we aimed to investigate the feasibility of using SSN to predict alkalinity through a comparative analysis of two distinct river basins in Türkiye. We evaluated the prediction performance of SSN and the spatial characterization of the basins using spatial data sets as model components. The models are based on moving-average (MA) functions and constructed by evaluating alkalinity observations at 90 locations in Kucuk Menderes River Basin and 224 locations in the Coruh River Basin, using torgegram, correlogram, principal component analysis, and exhaustive search algorithm for covariate selection. In this research, we evaluated the optimal combination of covariates obtained from 20 different variables within the context of five spatial data set classes: topography, geology, climate, land use, and anthropology. The outcomes of the model indicate that impact of anthropogenic activities on autocorrelation based on hydrologic distance, impact of spatial covariates on variance, and root mean square prediction error (RMSPE) values of the predictions in Kucuk Menderes are more effective than those in the Coruh. The findings from the model predictions show that modeling water quality on stream network scale by using topological relationships and autocorrelation based on hydrologic distance can be recommended for rivers affected by complex and detailed processes.

Tuning spatial parameters of Geographical Random Forest: the case of agricultural drought

Machine learning algorithms are widely used methods in geographical research. However, these algorithms are not properly exploiting the underlying spatial relationships present in the geographical data. One of the approaches, which addresses this problem, is based on an ensemble of local models, which are constructed from samples in close proximity to the location of prediction. This concept was applied to the Random Forest (RF) algorithm, creating a Geographical Random Forest (GRF). This study aims to further develop GRF by tuning the spatial parameters for each location in case of agricultural drought. In addition to tuning, the explanatory property of RF within the framework GRF is explored. Four machine learning models were constructed; regular RF, regular RF with spatial covariates, GRF, and GRF with the tuning of spatial parameters. Models were evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Although the decrease in RMSE in this very case is relatively small, the method may provide higher improvement with different datasets.

Non‐invasive mark–resight surveys for brown bears: Incorporating spatial information to improve landscape‐scale monitoring of density and distribution

Abstract Effective wildlife monitoring and management are reliant on unbiased estimators of population parameters. Most standard survey approaches ignore spatial pattern in the data although spatial relationships can be leveraged to produce more efficient designs for future surveys, thereby reducing project cost. Landscape‐scale surveys for brown bears are notoriously expensive and difficult to complete, leading to the development of a non‐invasive mark–resight aerial survey approach as a practical alternative to more commonly employed approaches for estimating brown bear density across large areas. Here we extend that model to accommodate spatial covariate information and explicitly model residual spatial autocorrelation. Using brown bear survey data from northwestern Alaska, USA from 2015 to 2021, we demonstrate how our extension can be useful in exploring ecological factors related to spatial variation in bear density and detection probability. We found that detection probability was higher in more rugged terrain, and occurrence probability was higher at mid‐elevations where denning habitat was more prevalent and in areas with more salmon streams. The descriptive model of residual autocorrelation confirmed that additional unmeasured factors were associated with bear distribution. While these findings were not unexpected, they are very useful when considering future survey effort. Using this information, we explored a hypothetical redesign of the 2021 brown bear survey that removed 50% of the survey subunits with elevation values below the mean, where bears were least likely to occur. We demonstrated that the resulting estimates were unbiased, suggesting that adopting the spatial approach to analysis could reduce overall project costs by ~20%–30% going forward. Overall, our work indicates that directly accounting for spatial pattern within the non‐invasive mark–resight framework has substantive advantages over the original non‐spatial approach and may be useful in increasing the amount of information available for brown bear populations.

Semiparametric M-quantile regression with measurement error in spatial covariates: an application to housing price modelling

Abstract Spatial data are becoming increasingly accessible to urban scientists, but these data are often prone to measurement error. Motivated by the analysis of the Milan (Italy) apartment market heterogeneity, we propose a semiparametric approach to adjust for the presence of measurement error in the covariates when estimating M-quantile regression. The M-quantile approach helps explain the heterogeneity across individual units, preserving robustness and efficiency in the estimates. The model’s parameters are estimated within a penalised likelihood framework and an analytical expression is proposed to estimate standard errors. Asymptotic properties of estimates are also provided.

Association between immigrant concentration and mental health service utilization in the United States over time: A geospatial big data analysis.

Immigrants (foreign-born United States [US] citizens) generally have lower utilization of mental health services compared with US-born counterparts, but extant studies have not investigated the disparities in mental health service utilization within immigrant population nationwide over time. Leveraging mobile phone-based visitation data, we estimated the average mental health utilization in contiguous US census tracts in 2019, 2020, and 2021 by employing two novel outcomes: mental health service visits and visit-to-need ratio (i.e., visits per depression diagnosis). We then investigated the tract-level association between immigration concentration and mental health service utilization outcomes using mixed-effects linear regression models that accounted for spatial lag effects, time effects, and covariates. This study reveals spatial and temporal disparities in mental health service visits and visit-to-need ratio among different levels of immigrant concentration across the US, both before and during the pandemic. Tracts with higher concentrations of Latin American immigrants showed significantly lower mental health service utilization visits and visit-to-need ratio, particularly in the US West. Tracts with Asian and European immigrant concentrations experienced a more significant decline in mental health service utilization visits and visit-to-need ratio from 2019 to 2020 than those with Latin American concentrations. Meanwhile, in 2021, tracts with Latin American concentrations had the least recovery in mental health service utilization visits. The study highlights the potential of geospatial big data for mental health research and informs public health interventions.