27 papers · ranked by Valyu relevance
Tobias Rüttenauer
This handbook chapter provides an essential introduction to the field of spatial econometrics, offering a comprehensive overview of techniques and methodologies for analysing spatial data in the social sciences. Spatial econometrics addresses the unique challenges posed by spatially dependent observations, where…
Bin Jiang
Geospatial analysis is very much dominated by a Gaussian way of thinking, which assumes that things in the world can be characterized by a well-defined mean, i.e., things are more or less similar in size. However, this assumption is not always valid. In fact, many things in the world lack a well-defined mean, and…
Chen, Chuan, Luo, Peng
Spatial dependence—referring to the correlation between variable values observed at different geographic locations—is one of the most fundamental characteristics of spatial data. Identifying and quantifying such dependence is critical for deepening our understanding of spatial phenomena. The presence of spatial…
Peng Luo, Yongze Song, Wenwen Li, Lingsheng Meng
Understanding the complex nature of spatial information is crucial for problem solving in social and environmental sciences. This study investigates how the underlying patterns of spatial data can significantly influence the outcomes of spatial predictions. Recognizing unique characteristics of spatial data, such as…
Abhimanyu Gupta, Qu Xi
We propose a series-based nonparametric specification test for a regression function when data are spatially dependent, the 'space' being of a general economic or social nature. Dependence can be parametric, parametric with increasing dimension, semiparametric or any combination thereof, thus covering a vast variety of…
Maryna Makeienko
This article provides symbolic analysis tools for specifying spatial econometric models. It firstly considers testing spatial dependence in the presence of potential leading deterministic spatial components (similar to time-series tests for unit roots in the presence of temporal drift and/or time-trend) and secondly…
Kaushi S. T. Kanankege, Julio Alvarez, Lin Zhang, Andres M. Perez
Spatiotemporal visualization and analytical tools (SATs) are increasingly being applied to risk-based surveillance/monitoring of adverse health events affecting humans, animals, and ecosystems. Different disciplines use diverse SATs to address similar research questions. The juxtaposition of these diverse techniques…
Jing Zhao, Yue Pu, Mohamed R. Abonazel
Spatial Autoregressive (SAR) models are widely used to analyze interactions among regions. However, the traditional model assumes a constant spatial autocorrelation coefficient, which fails to effectively capture spatial heterogeneity. To address this issue, we propose proposes a novel Spatial Single-Index Varying…
Jon Wang, Meng Lu
The use of geospatially dependent information, which has been stipulated as a law in geography, to model geographic patterns forms the cornerstone of geostatistics, and has been inherited in many data science based techniques as well, such as statistical learning algorithms. Still, we observe hesitations in…
Oluyemi A. Okunlola, Mohannad Alobid, Olusanya E. Olubusoye, Kayode Ayinde + 2 more
'Kayode Ayinde' 'Adewale F. Lukman' 'István Szűcs'] In this study, we propose a robust approach to handling geo-referenced data and discuss its statistical analysis. The linear regression model has been found inappropriate in this type of study. This motivates us to redefine its error structure to incorporate the…
R. Kelley Pace, Raffaella Calabrese
Automated valuation models (AVMs) are widely used by financial institutions to estimate the property value for a residential mortgage. The distribution of pricing errors obtained from AVMs generally show fat tails (Pender [23]; Demiroglu and James Management Science, 64(4), 1747-1760 [9]). The extreme events on the…
Zhe Jiang
—With the advancement of GPS and remote sensing technologies, large amounts of geospatial and spatiotemporal data are being collected from various domains, driving the need for effective and efficient prediction methods. Given spatial data samples with explanatory features and targeted responses (categorical or…
Guanpeng Dong, Richard Harris, Kelvyn Jones, Jianhui Yu + 1 more
'Yanguang Chen'] This paper develops a methodology for extending multilevel modelling to incorporate spatial interaction effects. The motivation is that classic multilevel models are not specifically spatial. Lower level units may be nested into higher level ones based on a geographical hierarchy (or a membership…
Niklas Lück, Robert Lohmayer, Stefan Solbrig, Stefan Schrod + 9 more
Advances in omics technologies have allowed spatially resolved molecular profiling of single cells, providing a window not only into the diversity and distribution of cell types within a tissue, but also into the effects of interactions between cells in shaping the transcriptional landscape. Cells send chemical and…
Michał Wawrzynowicz, Lechosław Kuczyński
The spatial organisation of breeding populations can influence fitness and population dynamics, yet the spatial expression of density dependence may vary with ecological context. We investigated whether predator abundance alters this expression in the northern lapwing (Vanellus vanellus), a loosely social…
Qi Liu, Chih-Yuan Hsu, Yu Shyr
The expeditious growth in spatial omics technologies enable profiling genome-wide molecular events at molecular and single-cell resolution, highlighting a need for fast and reliable methods to characterize spatial patterns. We developed SpaGene, a model-free method to discover any spatial patterns rapidly in large…
Gregory F Albery, Daniel J Becker, Josh A Firth, Matthew Silk + 99 more
High population density should drive individuals to more frequently share space and interact, producing better-connected spatial and social networks. Despite this widely-held assumption, it remains unconfirmed how local density generally drives individuals’ positions within wild animal networks. We analysed 34 datasets…
Adnane Nemri, Ovidiu Radulescu, Antoine Claessens, Thomas D. Otto
Despite the advent of spatial data science, including spatial biology, there exist few methods that study the distribution of points e.g. cells or individuals, accounting for both their own characteristics and environmental factors. We propose a new spatial entropy measure, termed the Regional Co-occurrence Entropy…
Sarah Chambliss, Carlos Pinon, Kyle Messier, Brian LaFranchi + 5 more
Disparity in air pollution exposure arises from variation at multiple spatial scales: along urbanto-rural gradients, between individual cities within a metropolitan region, within individual neighborhoods, and between city blocks. Here, we improve on existing capabilities to systematically compare urban variation at…
Eric J. Ward, Sean C. Anderson
1. Spatial and spatiotemporal models are increasingly used in ecology for a range of purposes, such as tracking population change, assessing species distributions, and modelling spatial processes. Many models, including generalized additive models (GAMs) and Gaussian random fields (fit via the Stochastic Partial…
Authors not listed
All-inorganic halide perovskites, especially CsPbBr3 microcrystals, are often considered to be optically stable and less defect-prone compared to their organometallic counterparts. Nevertheless, reports of photoluminescence (PL) blinking in bulk perovskite systems till date are restricted to organometallic halide…
Andrew J. Rominger
Why do rare species persist in ecosystems? Rare species seem to be at a disadvantage by pure probabilistic odds^1^ and perhaps also from poorly adapted species-environment and species-species interactions,^2^ though negative density-dependence may help buoy rare species.^3,4^ The question of rarity and persistence thus…
Authors not listed
Meteorological normalization is a key concept in studying anthropogenic effects on air pollutant concentrations and its temporal trends. While apparently successful in revealing anthropogenic effects and often used, there are downsides to the methods and limitations which should be taken into account when using it.…
Authors not listed
Predicting how often molecules collide in dilute solution remains a long-standing challenge often with several orders of magnitude difference between theoretical values and experimental values also among different experimental values. Traditional frameworks from Smoluchowski and Langmuir rely on the formation of stable…
Jixin Chen
Predicting the reaction kinetics, that is, how fast a reaction can happen in a solution, is essential information for many processes, such as industrial chemical manufacturing, refining, synthesis and separation of petroleum products, environmental processes in air and water, biological reactions in cells, biosensing…
Mei Tessum, Susan Anenberg, Zoe Chafe, Daven Henze + 4 more
To improve air quality, knowledge of the sources and locations of air pollutant emissions is critical. However, for many global cities, no previous estimates exist of how much exposure to fine particulate matter (PM2.5), the largest environmental cause of mortality, is caused by emissions within the city vs. outside…
Authors not listed
This article is the second in a two-part tutorial review on electronic spin-dependent dynamics. In Part I, we presented the fundamental theory within the adiabatic Born– Huang framework that describes the interaction between nuclear motion and the elec- tronic (spin and spatial) degrees of freedom. In particular, we…