Library · Signals, sensing & imaging
What Spatial Mapping & GIS got wrong, from 76 theses
Summary written by a model from the records below: The compiled records demonstrate various methodological failures and limitations encountered when applying statistical, machine learning, and geostatistical techniques to spatial datasets. Researchers frequently encountered issues with unaddressed spatial autocorrelation, invalid covariance assumptions in spatial interpolation, scale mismatches during geographic aggregation, and flawed spatial validation schemes. These records come from graduate theses, 2021 to 2026, each labeled with its degree where the repository states it, and each links to its thesis. They were extracted by language models reading the full text, so treat each as a lead to read, not a verdict.
Plus 8 problems the authors left open, listed at the end.
Failure modes, by theses reporting them
Select a mode to jump to its records.
Spatial interpolation and geostatistical kriging degrade under non-stationarity, sparse observations, or topological constraints
Geostatistical methods and Gaussian process models produce high extrapolation errors, spurious extrema, or unbounded predictive variance when applied to sparse or non-stationary data. They also fail when underlying physical processes follow river networks, complex flow geometries, or localized depressions that violate standard continuous covariance assumptions.
Tried and failed
Gaussian process regression applied to sparse discrete spatial count data. Outcome: worse than baseline. Reason: failed to model spatial distribution of sparse discrete counts effectively
Adaptive Robotic Search and Sampling of Sparse Natural Phenomena · MIT · PhD
Considered and rejected
Considered and rejected: Rejected Kriging for DPlow interpolation due to lack of spatial autocorrelation in variogram analysis.
Methods for evaluating the impact of pressure transients on water distribution pipe breaks · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected analytical distance-based Gaussian spatial covariance functions for estuarine water level interpolation due to their inability to represent channel topology and hydrodynamic phase lags.
A Scalable and Adaptable Coastal-Urban Flood Modeling Framework for Changing Climates · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected Ordinary Kriging, local regression-kriging, and Kriging with External Drift due to invalid spatial covariance assumptions on nested river network stations.
Suspended sediment transport in rivers: new indicators of transport dynamics for analysis of catchment and climate controls · Cranfield · PhD
Tried and failed
Gaussian process regression with squared-exponential kernel applied to spatial magnetic field mapping for localisation. Outcome: did not generalise. Reason: Gaussian process regression suffered high extrapolation errors outside the spatial training boundary
Enhanced Navigation Using Aerial Magnetic Field Mapping · Virginia Tech · Master's
Tried and failed
universal kriging with Gaussian semivariogram applied to spatial groundwater level interpolation. Outcome: data insufficient. Reason: spatial smoothing and grid resolution failed to resolve small-scale, localized depressions
Spatial analysis of early-development spring water levels in the Mississippi River Valley Alluvial Aquifer, Mississippi Alluvial Plain, South-Central United States · Texas Tech · Master's
Tried and failed
linear interpolation for gridded data applied to spatial wind velocity field modeling. Outcome: worse than baseline. Reason: linear interpolation inadequately captures continuous nonlinear spatial variations compared to kernel-based regression
Data-Driven Approach using Machine Learning for Real-Time Flight Path Optimization · Georgia Tech · PhD
Tried and failed
sequential Gaussian kriging spatial interpolation applied to continuous field reconstruction on grids. Outcome: worse than baseline. Reason: Generated spurious local extrema and exhibited excessive sensitivity to grid coarseness compared to splines
Hydrocarbon expulsion and scaling · UT Austin · PhD
Tried and failed
kriging with stationary semivariograms applied to spatial prediction under non-stationary noise. Outcome: did not generalise. Reason: produced radially unbounded errors and zero predictive variance under non-stationary ambient noise
Communication-Aware, Scalable Gaussian Processes for Decentralized Exploration · Virginia Tech · Doctorate
Tried and failed
precision-weighted aggregation of local gaussian processes applied to spatial satellite temperature prediction. Outcome: did not generalise. Reason: aggregation degraded predictions across large unobserved spatial extrapolation regions
Precision Aggregated Local Models · Virginia Tech · Doctorate
Tried and failed
local search radius ordinary kriging interpolation applied to spatial prediction with sparse observations. Reason: Prediction locations lacked sufficient conditioning points within the fixed local search radius.
Worlds Collide through Gaussian Processes: Statistics, Geoscience and Mathematical Programming · Virginia Tech · Doctorate
Lost to a baseline
On real Hengstlage deformation data, Ordinary Kriging achieved a slightly lower prediction RMSE (0.44 mm/year) than MBA (0.47 mm/year) due to Kriging explicitly exploiting spatial correlation via the variogram.
A Pipeline for Modeling Point-Sampled Surfaces: From Preprocessing to Quality Assessment · Leibniz Universität Hannover · PhD
Lost to a baseline
Under spatial leave-one-out cross-validation (SLOO-CV) on Malawi and Tanzania PHIA data, kernel models (CK/IK) were beaten by the simpler Besag model (Malawi SLOO CRPS: 19.3 Besag vs 29.0 CK / 28.3 IK).
Bayesian spatio-temporal methods for small-area estimation of HIV indicators · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected ordinary kriging for spatial interpolation because irregular, sparse field-parcel centroids provided insufficient data points to compute usable semi-variograms.
Estimating field-scale soil moisture using SAR remote sensing and the COSMOS-UK network · Cranfield · PhD
Considered and rejected
Considered and rejected: Rejected sparse and local approximations for Gaussian Process Regression (kriging) because approximations introduce errors and information loss for spatial parameter estimation.
Visual Analytics for High Dimensional Simulation Ensembles · Virginia Tech · Doctorate
Considered and rejected
Considered and rejected: Rejected Kriging spatial interpolation in favor of Inverse Distance Weighting (IDW) because Kriging required cross-variable spatial correlation not suited to the univariate layout.
Assessment of groundwater aquifer impact from artificial lagoons and the reuse of wastewater in Qatar · Nottingham · PhD
Spatial clustering algorithms and simplified distance metrics fail when spatial density varies or geographic movement is constrained
Fixed-density clustering methods like DBSCAN and simplified distance formulations break down when geographic density varies widely across space or movement is restricted by network topology. Subsetting to high-density areas or relying on straight-line distance metrics often fails to capture fine-grained relational connectivity, merges distinct clusters, or introduces severe boundary artifacts.
Tried and failed
distance-based Gaussian kernel spatial graph construction applied to spatiotemporal time-series forecasting. Outcome: worse than baseline. Reason: failed to capture relational correlations between distant nodes
Graph-based Time-series Forecasting in Deep Learning · Virginia Tech · Doctorate
Lost to a baseline
Potential connectivity and graph-network connectivity metrics consistently performed worse than structural aggregation metrics across all three spatial scales in predicting bird species richness.
Examining the Role of Landscape Connectivity Metrics on Biodiversity Outcomes in Urban Ecosystems · Carleton University · PhD
Lost to a baseline
RREuc model performed worse than simpler spatial models in simulation due to mismatch with rectangular plot geometry.
STABILITY OF TRAITS ACROSS ENVIRONMENTS USING IMAGE PHENOTYPING AND GENOTYPING · Cornell · PhD
Tried and failed
multivariate regression on stratified spatial clusters applied to transit ridership determinants. Reason: density metric produced counterintuitive negative coefficients across models
Understanding Heterogeneous Effect of Built-environment on Shenzhen Metro Ridership by Classifying Stations · Cornell · Master's
Tried and failed
spatial subsetting to high-density regions applied to human mobility spatial flow models. Outcome: did not generalise. Reason: density filtering did not align regional parameter estimates or improve gridded-scale likelihood
The role of seasonal population movements in malaria transmission and control in sub-Saharan Africa · Imperial · PhD
Tried and failed
DBSCAN clustering applied to spatial trajectory clustering with varying density. Reason: fixed density threshold merged distinct clusters in regions of varying spatial density
A Data-driven Methodology for Aircraft Trajectory Analysis to Improve Mid-air Conflict Detection in Terminal Airspace · Georgia Tech · PhD
Tried and failed
network distance over Euclidean distance metric applied to spatial econometric regression models. Outcome: worse than baseline. Reason: reduced statistical significance and provided no meaningful predictive improvement over straight-line distance
Transit-Oriented Development or Development-Oriented Transit: Measuring the Effect of Proximity to LA Metro Rail on Residential Construction · Harvard · Master's
Tried and failed
threshold-based domain truncation with density renormalization applied to spatial risk assessment models. Outcome: worse than baseline. Reason: Renormalizing probability density after removing low-density areas artificially inflated exposure in remaining high-density regions
Integration and Effects of Three-Dimensional Terrain Models on Potential Crash Areas of Aircraft · Virginia Tech · Master's
Tried and failed
density-based spatial clustering of trajectory data applied to spatial trajectory anomaly detection. Outcome: did not generalise. Reason: dynamic airspace constraints and environmental perturbations degraded clustering performance
Development and evaluation of data analytics and machine learning approaches for enhanced urban air mobility operations · Cranfield · PhD
Considered and rejected
Considered and rejected: Rejected K-means clustering in favor of grid-based spatial city separation after grid zoning demonstrated a 26.7% overall variance reduction across 60 days of demand data
Urban Logistics and Last Mile applications: Models and methods to deal with demand uncertainty · Politecnico di Torino · PhD
Considered and rejected
Considered and rejected: Rejected discrete polygonal grid or non-overlapping area partitioning for neighborhood analyses in favor of continuous overlapping 200 m radius circular neighborhoods with 2D Gaussian process spatial autocorrelation modeling.
Cultural evolution in the wild: tracking the landscape of learning in bird song · Oxford · PhD
Tried and failed
nearest neighbor spatial interpolation of meteorological data applied to hydrological watershed modeling. Outcome: worse than baseline. Reason: failed to capture spatial variability compared to distance-weighted methods, worsening discharge simulation metrics
Improving Watershed Models to Achieve a Better Prediction of Water Quantity and Quality · Virginia Tech · Doctorate
Tried and failed
similarity-based parameter regionalization applied to hydrological models on distant ungauged catchments. Outcome: did not generalise. Reason: spatial proximity breakdown over larger geographic distances (>20 km) led to poor predictive performance
Improving Watershed Models to Achieve a Better Prediction of Water Quantity and Quality · Virginia Tech · Doctorate
Spatial autoregressive specifications and spatial random effects introduce estimation instability or fail to beat simpler baselines
Incorporating conditionally autoregressive priors or spatial random effects often degrades treatment effect estimation or yields worse goodness of fit than simpler non-spatial baselines. In addition, strong spatial dependence can trigger numerical phase transitions, unidentifiable variance partitioning between random effects, or severely inflated Type I and Type II error rates.
Tried and failed
generalized additive models with spatial weight correction applied to species distribution modeling. Reason: residuals continued to exhibit significant spatial autocorrelation despite spatial weight corrections
TIME FLIES: INSIGHTS FROM HISTORICAL AND CONTEMPORARY DATA ON THE TEMPORAL TRENDS AND CONSERVATION OF HAWAIIAN DROSOPHILIDS · Cornell · PhD
Tried and failed
spatial-dependence occupancy modeling applied to transect-based species sign surveys. Outcome: worse than baseline. Reason: Spatial autocorrelation model formulation lacked empirical support over standard single-season occupancy baseline
CONSERVATION AND MANAGEMENT OF THE ANDEAN BEAR IN COLOMBIA, ECUADOR, AND PERU. FROM LOCAL TO REGIONAL SCALES. · Cornell · PhD
Tried and failed
centered autologistic model applied to spatial binary data modeling. Outcome: unstable. Reason: experiences unintended phase transitions under strong spatial dependence, heavily penalizing configurations matching the mean trend
Bayesian spatial models for discrete data : methodological advances with applications in urban sociology · UT Austin · PhD
Tried and failed
Conditional autoregressive models for spatial inference applied to spatial regression with autocorrelated covariates. Reason: Strong autocorrelation and large spatial scale inflated Type I error rates across models
Conditional autoregressive models : implications for inference · UT Austin · PhD
Tried and failed
adding conditionally autoregressive spatial random effects applied to spatial causal effect estimation models. Outcome: worse than baseline. Reason: deteriorated treatment effect estimation accuracy in simulated spatial confounding environments
Spatial applications of Markov random fields and neural networks for spatio-temporal denoising, causal inference and reinforcement learning · UT Austin · PhD
Tried and failed
full-rank multinomial Markov random fields applied to spatial categorical data modeling. Reason: The reference category baseline forces its spatial auto-correlation to near zero, creating category-relabeling asymmetry
Symmetry, stability, and spatio-temporal dependence in multinomial Markov random field models · Iowa State · PhD
Lost to a baseline
ICAR model had substantially higher Type II error rates (up to 0.681) than the non-spatial baseline (0.374) in high autocorrelation conditions.
Conditional autoregressive models : implications for inference · UT Austin · PhD
Lost to a baseline
Under full covariate availability with informative sampling, unit-level spatial Betabinomial and Binomial models achieved lower RMSE (2.62 x100) than Spatial SMA (3.03 x100)
Estimating subnational health and demographic indicators using complex survey data · University of Washington · PhD
Lost to a baseline
Dynamic linear model (SpDynLM) produced higher hold-out prediction error (RMSPE = 0.868) than Graphical Matern (RMSPE = 0.800) in the AR(1) spatial time-series simulation Set 3A
Topics in Modeling of Multivariate Mixed Data Types and Highly Multivariate Spatial Data · Johns Hopkins · PhD
Lost to a baseline
In spatial binary simulations with no repetition (λw = 1), the naive spatial binomial probit model yielded lower MSE for spatial dependence λη than the proposed SPPM (e.g., MSE 0.242 vs 1.427 at λη = 0.1, and MSE 0.511 vs 7.830 at λη = 0.6).
Spatial modeling of repeated events · University of Manitoba · PhD
Considered and rejected
Considered and rejected: Rejected the Besag-York-Mollié (BYM) convolution model due to fixed spatial autocorrelation (rho_s = 1), non-identifiable separation of spatial and independent random effects, slow MCMC convergence, and unreliable parameter estimation.
Conditional autoregressive models : implications for inference · UT Austin · PhD
Standard non-spatial regressions fail because residual spatial autocorrelation violates independence assumptions
Ignoring spatial dependence in regression residuals inflates estimated regression coefficients, underestimates standard errors, and leads to pseudoreplication. Consequently, flat ordinary least squares and non-spatial generalized linear models are routinely outperformed or rejected in favor of models that explicitly account for spatial structures.
Tried and failed
non-spatial logistic regression applied to geocoded epidemiological disease data. Reason: significant spatial autocorrelation in model residuals violated independence assumptions
Enhancing Electronic Health Record Data For Population Health Studies · Penn · PhD
Tried and failed
difference-in-difference-in-differences regression without spatial correction applied to spatially distributed panel data. Reason: uncorrected spatial autocorrelation inflated regression coefficients and underestimated standard errors
TOWARD A JUST TRANSITION: THE IMPLICATIONS OF URBAN FREIGHT AND PORTS IN THE ENVIRONMENT, ENERGY, AND HEALTH · Cornell · PhD
Lost to a baseline
OLS regression underperformed SERM across all metrics (e.g., OLS R2 of 0.184 vs SERM R2 of 0.255, and higher AIC of 28,051.7 vs 27,984.1 for devch vs slrdevch) due to spatial autocorrelation
Risk and Development Along the California Coast: A Study of Projected Sea-Level Rise and Land-Use · Texas State University · Master's
Lost to a baseline
Habitat-based GLM (AIC = 86.09) was outperformed by spatial GLM based on latitude and longitude (AIC = 75.75) for predicting colony occupancy.
Habitat Associations and Movement Tracking of North American Black Terns (Chlidonias niger) · Saskatchewan · Master's
Lost to a baseline
All-sites GLM was beaten by site-level GLM on ground-truthing data (AUC 0.66 vs 0.69) due to local spatial variation in habitat selection.
Using Remote Sensing Data to Predict Habitat Occupancy of Pine Savanna Bird Species · Virginia Tech · Master's
Considered and rejected
Considered and rejected: Occupancy modelling was abandoned in favor of logistic regression (GLM) for main landscape analyses due to failure of occupancy models to converge under complete separation and inability to account for spatial autocorrelation
Listening in on the forest: use of bioacoustics to preserve soundscapes and rare species · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected non-spatial linear regression models for large-scale herbarium data because spatial autocorrelation caused pseudoreplication and more than doubled the estimated phenological shift (-1.34 vs -0.56 days/decade).
Effects of global change on plants: tracing the footprints of climate warming and land use from herbaria to forest understories. · Tübingen · PhD
Considered and rejected
Considered and rejected: National-level OLS regression was rejected due to significant residual spatial autocorrelation violating independence assumptions.
Greater Access to Recreational Resources is Associated with More Leisure-time Physical Activity Engagement in Counties Across the United States · University of Oregon · PhD
Considered and rejected
Considered and rejected: Rejected non-spatial regression and classical local models (OLS, KNN, GWR/MGWR) due to inability to model non-linear spatial dependencies and instability with multicollinear/noisy data.
DepGCN: A Spatial Deep Learning Framework Based on Graph Convolutional Networks for Modeling Depression Prevalence · Queen's University · Master's
Considered and rejected
Considered and rejected: Rejected standard flat Ordinary Least Squares regression for regional correlational analysis due to significant spatial autocorrelation violating error independence; adopted spatial lag regression models instead.
Multi-level Methods for Estimating Community Language from Social Media with User and Community Sociodemographics · Penn · PhD
Considered and rejected
Considered and rejected: Rejected standard linear regression without spatial consideration due to strong spatial autocorrelation (Moran's I = 0.62) in sub-district income data
Evaluating Industrial Activities and Its Social/Economic Impact in a Developing Country with Remotely Sensed Data: A Case Study of Eastern Economic Corridor, Thailand · Queen's University · PhD
Coarse spatial aggregation and mesh discretization obscure localized processes and degrade simulation accuracy
Aggregating spatial data into coarse geographic units or using coarse simulation meshes smooths away localized environmental features and transient flow dynamics. Conversely, simply increasing spatial resolution or introducing excessively fine spatial sub-areas can introduce severe noise or fail to improve simulation accuracy.
Tried and failed
average pooling spatiotemporal sensor features applied to traffic incident impact duration prediction. Outcome: worse than baseline. Reason: simple pooling discarded fine-grained spatial density and temporal dynamics necessary for accurate prediction
On Modeling Dependency Dynamics of Sequential Data: Methods and Applications · Virginia Tech · Doctorate
Tried and failed
coarse regional reporting data for spatial prediction applied to chemical contaminant leaching across geographic zones. Outcome: did not generalise. Reason: zip code level aggregation lacked sufficient spatial resolution and accuracy to predict localized chemical detections
Assessment of Pesticide Leachability on Long Island Using the Theoretical Groundwater Ubiquity Score (TGUS) Model · Cornell · Master's
Tried and failed
increasing spatial resolution of input data applied to hydrological sediment transport modeling. Reason: higher-resolution data did not yield consistent improvements in simulation accuracy
Comprehensive Watershed-Scale PFOS Transport Modeling in the San Antonio River Watershed using SWAT+ · Texas State University · Master's
Tried and failed
high-resolution urban heterogeneity mapping in weather models applied to tropical storm precipitation forecasting. Outcome: worse than baseline. Reason: failed to fix rainfall underestimation and degraded the spatial accuracy of simulated precipitation clusters
Improved urban extreme weather simulation by capturing urban heterogeneity · UT Austin · PhD
Tried and failed
statistical downscaling of global climate models applied to regional precipitation variability in complex terrain. Outcome: did not generalise. Reason: models smoothed and flattened spatial precipitation variability compared to observed local station records
Projecting Planning-Related Climate Impact Drivers for Appalachian Public Health Support · Virginia Tech · Master's
Tried and failed
coarse spatial mesh discretization applied to transient porous media flow simulation. Outcome: worse than baseline. Reason: insufficient spatial resolution failed to capture transient flow dynamics accurately over time
Integrated modelling of the clogging processes of plastic grid permeable pavement · Cranfield · PhD
Tried and failed
model equivalence testing with coarse spatial inputs applied to third-party spatial risk assessment. Outcome: did not generalise. Reason: coarse spatial resolution introduced variance exceeding equivalence bounds
Assessment of Multiple Third-Party Risk Models Incorporating Uncertainty Quantification and Sensitivity Analysis · Virginia Tech · Master's
Tried and failed
GIS digital elevation models for hydrological modeling applied to excavated terrain and plateau surface hydrology. Outcome: data insufficient. Reason: Coarse spatial resolution and missing historical imagery failed to resolve excavation micro-topography and hydrology
From Wasteland to Biocultural Heritage: Negotiation by Design in Khotale, Konkan, India · Cornell · Master's
Tried and failed
general circulation models for local climate projections applied to regional scale climate impact modeling. Outcome: data insufficient. Reason: spatial resolution was too coarse to capture regional variations accurately
Innovative methodology for the assessment of thermal comfort and climate resilience in social housing in Coahuila, Mexico · Nottingham · PhD
Lost to a baseline
Spatial error models based on watershed sources achieved lower R2 values (0.24-0.38) on state-level data compared to the prior UCMR 3 national analysis (R2 of 0.40-0.62).
Trends and Disparities in Contamination by Per- and Polyfluoroalkyl Substances in United States Community Water Systems · Harvard · PhD
Considered and rejected
Considered and rejected: Rejected using too many distinct spatial sub-areas for assigning lateral conductivity compensation factors Λx,y on the hill because it degraded model performance.
Data assimilation for integrated subsurface flow problems - dealing with biases and different observation types · Leibniz Universität Hannover · PhD
Considered and rejected
Considered and rejected: Rejected modeling spatial upstream-downstream hydrological dependencies between cells in watershed nitrate routing due to extreme data intensity and introduction of high non-linearity.
Insights from stochastic programs on aligning farmer profit motive with environmental goals · Iowa State · PhD
Spatial validation and sampling strategies suffer from spatial data leakage or severe extrapolation penalties
Conventional cross-validation strategies produce misleading performance estimates because random partitioning leaks spatial autocorrelation across training and test folds. At the same time, spatial block cross-validation forces models into extreme extrapolation, while unthinned occurrence records and raw coordinate features introduce severe sampling bias and artificial boundaries.
Tried and failed
MaxEnt species distribution modeling with environmental covariates applied to local wildlife spatial distribution. Outcome: data insufficient. Reason: Covariates lacked spatial variation across the study area and introduced sampling bias
Biogeography & Conservation Status of Bats in Chautauqua County, NY · University at Buffalo · PhD
Tried and failed
including spatial coordinates as tree model features applied to global spatial distribution regression. Reason: produced unrealistic spatial artefacts and discontinuities in continuous global reconstruction maps
Environmental pollution as a marine tracer: measuring and modelling anthropogenic lead in the ocean · Imperial · PhD
Lost to a baseline
Spatial filtering (2-km and 10-km) and kernel density bias files performed worse (higher commission/overfitting errors) than the baseline unmanipulated dataset in MaxEnt.
The conservation status of Owston’s Civet Chrotogale owstoni · Nottingham · Master's
Considered and rejected
Considered and rejected: Rejected analyzing the Global Moran's I test for Spatial Autocorrelation (SA), AICc, JB, and VIF models in exploratory regression summaries due to lack of spatial correlation/autocorrelation.
The Missing Link: How Languages Can Influence Conflict · Harvard · Master's
Considered and rejected
Considered and rejected: Rejected using unthinned citizen-science (eBird) occurrence records due to extreme spatial autocorrelation in Costa Rica birding hotspots.
Fantastic Birds and Where to Find Them · University of Nevada, Reno · PhD
Considered and rejected
Considered and rejected: Rejected correcting for spatial autocorrelation in prairie forage biomass due to inherent natural vegetation patchiness and invasion distribution.
Microbial Community Assembly Processes in Agricultural and Natural Ecosystems · Saskatchewan · PhD
Tried and failed
Cross-validation error estimation for spatial regression applied to spatial property prediction from covariates. Reason: Cross-validation produced more pessimistic error estimates than independent validation.
Precision land surface analysis and machine learning for enhanced soil maps: Strengthening the foundation for agroecosystems research · Iowa State · PhD
Tried and failed
rule-based regression with spatial features applied to soil texture mapping. Outcome: overfit. Reason: Standard cross-validation produced inflated metrics that inversely correlated with true independent spatial validation accuracy.
An approach to map soil texture class on the Iowan Erosion Surface · Iowa State · Master's
Tried and failed
global fixed window size selection via cross-validation applied to spatiotemporal epidemic growth rate estimation. Outcome: did not generalise. Reason: fails to balance speed-accuracy tradeoffs across heterogeneous local spatial and temporal outbreak dynamics
Healthcare data analytics for social good · Georgia Tech · PhD
Lost to a baseline
Under yearly block 10-fold cross-validation on the 474-station network (50% completeness), spatio-temporal kriging (19.66 mm RMSE) lost its performance advantage over purely spatial kriging (20.66 mm RMSE).
Spatio-temporal model estimation and imputation of rainfall networks · University College Dublin · PhD
Considered and rejected
Considered and rejected: Rejected standard random and spatial/block k-fold cross-validation because random CV yields overly easy predictions (information leakage) while block CV forces severe extrapolation
Spatially-informed machine learning for augmented subsurface characterization, modeling, and interpretation · UT Austin · PhD
Considered and rejected
Considered and rejected: Rejected using latitude and longitude features in hierarchical clustering for rainfall up-scaling because spatial coordinates dominated and degraded precipitation pattern grouping.
Underpinning research on the dynamical aspects of one-dimensional nonlinear lattices · University College Dublin · PhD
Multi-criteria spatial decision making and facility siting frameworks exclude viable options or lack necessary localized data
Spatial multi-criteria analysis and techno-economic layout optimization frequently fail because critical real estate pricing or fine-scale hazard databases are missing or inaccessible. Furthermore, arbitrary feature selection and weighting schemes can generate site rankings inconsistent with empirical usage patterns or exclude deployed projects from Pareto-optimal sets.
Tried and failed
multicriteria spatial analysis with real estate pricing applied to facility location selection. Outcome: data insufficient. Reason: reliable real estate pricing data was too difficult to access reliably
Locating mobility hubs in college towns: A case study of Ames, Iowa · Iowa State · Master's
Tried and failed
VIKOR multi-criteria decision making with equal weights applied to spatial multi-criteria site suitability ranking. Reason: produced rankings with low correlation compared to other multi-criteria decision making methods
Location planning of future offshore wind farms to account for climate change-driven extreme weather events · Oxford · PhD
Tried and failed
standardized environmental hazard screening tools applied to institutional sites and regional hazards. Outcome: data insufficient. Reason: spatial databases omitted specific facilities and lacked coverage of localized rural environmental hazards
Tried and failed
correlation-based feature selection for spatial siting applied to renewable energy resource placement. Outcome: worse than baseline. Reason: Produced less total generation and higher spot-market prices than cost or price metric baselines.
Understanding The Operational Impact Of Storage And Renewables On The Electricity Provision Of The Future · Penn · PhD
Left open by the authors
Problems the authors named and did not get to.
Left open
Incorporate spatial correlation structures into the Bayesian population synthesis model framework. Blocker: Lacks specific mathematical formulation, targets, and spatial structure definitions
Inference for Populations: Uncertainty Propagation via Bayesian Population Synthesis · Virginia Tech · Doctorate
Left open
Model the spatial variability of shear strength parameters within individual geologic units for regional landslide hazard assessment. Blocker: Lacks specific target geologic units, datasets, and spatial statistical methodology.
Regional earthquake-induced landslide assessments using a data-informed probabilistic approach · UT Austin · PhD
Left open
Model spatial autocorrelation and implement hierarchical multi-level modeling across census tracts, counties, and metro regions using MGF sub-geographies. Blocker: None
Using land as a lens : multi-scalar analysis of metropolitan structure and growth · UT Austin · PhD
Left open
Incorporate spatial econometric models to account for spatial autocorrelation in soil organic carbon measurements across agricultural landscapes. Blocker: Requires the specific soil sampling spatial dataset and landscape field data from the study region.
Economic Analyses of Soil Carbon Management and Regenerative Agricultural Practices in Semi-Arid Ecoregions · Texas Tech · Doctorate
Left open
Investigate why the Q* fitness balance criterion derived from non-spatial models fails in specific spatial regions of aquatic stoichiometric PDE systems. Blocker: None
New models and numerical methods for partial differential equations applied to spatial stoichiometric aquatic ecosystems · Texas Tech · PhD
Left open
Develop distributed hypothesis testing algorithms under communication constraints for spatially correlated or non-independent node observations. Blocker: Lacks specific target error bounds, correlation model formulation, and concrete algorithmic approach
Time vs. Truth: Age-Distortion Tradeoffs and Strategies for Distributed Inference · EPFL · PhD
Left open
Extend the epidemiological model to include spatial dynamics and multi-year environmental variability. Blocker: Lack of specific mathematical formulation or data parameters for spatial and environmental variability
Using Modelling to Optimise the Use of Biological Control Agents Against Soil-Borne Plant Pathogens · Cambridge · PhD
Left open
Derive a formal mathematical relationship linking spatial autocorrelation and overdispersion across multiple length scales. Blocker: None
Experimental design and analysis for high-parameter spatial omics · MIT · PhD
Who has worked on this
Authors of the public theses cited on this page, most-cited first.
- Roja Kaveh Garna · Virginia Tech · Doctorate · 2022
Improving Watershed Models to Achieve a Better Prediction of Water Quantity and Quality - Bahareh Mohammadivojdan · Leibniz Universität Hannover · PhD · 2026
A Pipeline for Modeling Point-Sampled Surfaces: From Preprocessing to Quality Assessment - Andrew Karl Habrich · Carleton University · PhD · 2026
Examining the Role of Landscape Connectivity Metrics on Biodiversity Outcomes in Urban Ecosystems - Alexander Joseph Callo · Virginia Tech · Master's · 2026
Integration and Effects of Three-Dimensional Terrain Models on Potential Crash Areas of Aircraft - Himmat Basnet · Texas State University · Master's · 2026
Comprehensive Watershed-Scale PFOS Transport Modeling in the San Antonio River Watershed using SWAT+ - Giacomo Conde · Virginia Tech · Master's · 2026
Assessment of Multiple Third-Party Risk Models Incorporating Uncertainty Quantification and Sensitivity Analysis - Jahred Liddie · Harvard · PhD · 2025
Trends and Disparities in Contamination by Per- and Polyfluoroalkyl Substances in United States Community Water Systems - Xin Shen · Cornell · Master's · 2025
Assessment of Pesticide Leachability on Long Island Using the Theoretical Groundwater Ubiquity Score (TGUS) Model - Carlos Ingnacio Jara Arriagada · Imperial · PhD · 2025
Methods for evaluating the impact of pressure transients on water distribution pipe breaks - Arianna Olivelli · Imperial · PhD · 2025
Environmental pollution as a marine tracer: measuring and modelling anthropogenic lead in the ocean - Jae hun Shin · Cranfield · PhD · 2025
Suspended sediment transport in rivers: new indicators of transport dynamics for analysis of catchment and climate controls - Fahime Karami · Queen's University · Master's · 2025
DepGCN: A Spatial Deep Learning Framework Based on Graph Convolutional Networks for Modeling Depression Prevalence
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