Selected Challenges From Spatial Statistics For Spatial Econometricians

Griffith and Paelinck (2011) present selected non-standard spatial statistics and spatial econometrics topics that address issues associated with spatial econometric methodology. This paper addresses the following challenges posed by spatial autocorrelation alluded to and/or derived from the spatial statistics topics of this book: the Gaussian random variable Jacobian term for massive datasets; topological features of georeferenced data; eigenvector spatial filtering-based georeferenced data generating mechanisms; and, interpreting random effects.Artykuł prezentuje wybrane, niestandardowe statystyki przestrzenne oraz zagadnienia ekonometrii przestrzennej. Rozważania teoretyczne koncentrują się na wyzwaniach wynikających z autokorelacji przestrzennej, nawiązując do pojęć Gaussowskiej zmiennej losowej, topologicznych cech danych georeferencyjnych, wektorów własnych, filtrów przestrzennych, georeferencyjnych mechanizmów generowania danych oraz interpretacji efektów losowych

On the spatial modelling of mixed and constrained geospatial data

Spatial uncertainty modelling and prediction of a set of regionalized dependent variables from various sample spaces (e.g. continuous and categorical) is a common challenge for geoscience modellers and many geoscience applications such as evaluation of mineral resources, characterization of oil reservoirs or hydrology of groundwater. To consider the complex statistical and spatial relationships, categorical data such as rock types, soil types, alteration units, and continental crustal blocks should be modelled jointly with other continuous attributes (e.g. porosity, permeability, seismic velocity, mineral and geochemical compositions or pollutant concentration). These multivariate geospatial data normally have complex statistical and spatial relationships which should be honoured in the predicted models. Continuous variables in the form of percentages, proportions, frequencies, and concentrations are compositional which means they are non-negative values representing some parts of a whole. Such data carry just relative information and the constant sum constraint forces at least one covariance to be negative and induces spurious statistical and spatial correlations. As a result, classical (geo)statistical techniques should not be implemented on the original compositional data. Several geostatistical techniques have been developed recently for the spatial modelling of compositional data. However, few of these consider the joint statistical and/or spatial relationships of regionalized compositional data with the other dependent categorical information. This PhD thesis explores and introduces approaches to spatial modelling of regionalized compositional and categorical data. The first proposed approach is in the multiple-point geostatistics framework, where the direct sampling algorithm is developed for joint simulation of compositional and categorical data. The second proposed method is based on two-point geostatistics and is useful for the situation where a large and representative training image is not available or difficult to build. Approaches to geostatistical simulation of regionalized compositions consisting of several populations are explored and investigated. The multi-population characteristic is usually related to a dependent categorical variable (e.g. rock type, soil type, and land use). Finally, a hybrid predictive model based on the advanced geostatistical simulation techniques for compositional data and machine learning is introduced. Such a hybrid model has the ability to rank and select features internally, which is useful for geoscience process discovery analysis. The proposed techniques were evaluated via several case studies and results supported their usefulness and applicability

Comparing spatial patterns

The second author would like to acknowledge Natural Sciences and Engineering Research Council of Canada for funding this paper.The comparison of spatial patterns is a fundamental task in geography and quantitative spatial modelling. With the growth of data being collected with a geospatial element, we are witnessing an increased interest in analyses requiring spatial pattern comparisons (e.g., model assessment and change analysis). In this paper, we review quantitative techniques for comparing spatial patterns, examining key methodological approaches developed both within and beyond the field of geography. We highlight the key challenges using examples from widely known datasets from the spatial analysis literature. Through these examples, we identify a problematic dichotomy between spatial pattern and process—a widespread issue in the age of big geospatial data. Further, we identify the role of complex topology, the interdependence of spatial configuration and composition, and spatial scale as key (research) challenges. Several areas ripe for geographic research are discussed to establish a consolidated research agenda for spatial pattern comparison grounded in quantitative geography. Hierarchical scaling and the modifiable areal unit problem are highlighted as ideas which can be exploited to identify pattern similarities across spatial and temporal scales. Increased use of “time-aware” comparisons of spatial processes are suggested, which properly account for spatial evolution and pattern formation. Simulation-based inference is identified as particularly promising for integrating spatial pattern comparison into existing modelling frameworks. To date, the literature on spatial pattern comparison has been fragmented, and we hope this work will provide a basis for others to build on in future studies.PostprintPeer reviewe

Fishing from space : mackerel fishing in Icelandic waters and correlation with satellite variables

As concentrations of measured CO2 in the atmosphere reach a record high it is important to attempt all possible efforts to reduce the emissions of greenhouse gases (GHG) in all aspects of industry. The fishing sector contributes 15% of total GHG emission in Iceland, with the majority originating from fishing vessels using fossil fuel. The relationship between catching locations of Atlantic Mackerel (Scomber scombrus) in Icelandic waters and satellite remote sensing variables was explored. The aim was to provide information for possible fish¬eries forecasting, which could facilitate reduced energy consumption in Icelandic fishing vessels. The hypothesis was that satellite variables were a valuable source of information for determining viable fishing grounds in Icelandic waters. The variables explored were sea surface temperature (SST), chlorophyll (CHL), photosyn-thetically available radiation (PAR), water leaving radiance (L443) and down welling diffusion attenuation coefficient (kd490). The spatial resolution was about 4.6 km and temporal resolution 1 day. Effects of decreased spatial and temporal resolution were also explored. Binomial generalized additive models were created to identify the possible relationship with fishing locations represented as absence or presence of mackerel catches. Seven day PAR was the strongest single variable, explaining 47% of deviance, with the spatial variables latitude and longitude incorporated. The most successful multiple variable models included one or seven day averages of PAR and SST and seven day averages of L443, explaining 48% of deviance. Decreasing temporal resolution to 7 days improves the predictive ability of all variables. Decreasing spatial resolution to 3*3 cells does not decrease or increase the predictability to any extent. In order to estimate the usefulness of global data sets in local situations, a correlation of observed and remotely sensed CHL in Icelandic waters was estimated. Results on a minor sample size revealed a strong significant correlation, suggesting that global datasets were useful in local situations around Iceland. The satellite variables explored significantly contribute to a model explaining the absence and presences locations for mackerel fishing in Icelandic waters. Mackerel catches were most successful in a temperature range of 7.5°-13°C where there were high amounts of incoming visible solar radiation and intermediate concentration of phytoplankton. Clear waters due to little absorption as well as turbulent water with high scattering also had effects. This suggested that mackerel caught in Icelandic waters was more dependent on visual foraging than previously considered.Styrkur CO2 í andrúmslofti mældist í fyrsta sinn yfir 400 ppm í maí 2013. Með Kyotobókuninni frá 1997 hafa þjóðir heimsins hafa skuldbundið sig til að draga úr losun gróðurhúsalofttegunda. Ísland er þar á meðal. Sjávarútvegurinn leggur til um 15 % af heildarlosun gróðurhúsalofttegunda á Íslandi. Meiri hlutinn kemur til vegna brennslu jarðefnaeldsneytis skipaflotans. Ein af tíu lykilaðgerðum Umhverfis- og auðlindaráðuneytisins til að draga úr losun gróðurhúsalofttegunda er að leita leiða til að draga úr útblæstri íslenska fiskiskipaflotans. Tengsl milli veiðistaða makríls (Scomber scombrus) á Íslandsmiðum og fjarkönnunarganga frá gervitunglum var könnuð. Markmiðið var að afla upplýsinga fyrir mögulegar fiskiveiðispár, sem geta stuðlað að minni orkunotkun fiskiskipa. Tilgátan var sú að fjarkönnunargögn úr gervitunglum séu uppspretta gagnlegra upplýsinga til að ákvarða vænlegar fiskislóðir á Íslandsmiðum. Fimm gervitunglabreyturnar voru kannaðar: yfirborðshiti sjávar (SST), magn blaðgrænu (CHL), styrkur ljóstillífunargeislunar (PAR), full staðlaður geislunarljómi endurkastaðs ljóss frá vatni (L443) og stuðull fyrir niðurstreymi dreifðrar geislunar í vatni (kd490). Svæðisupplausn gervitunglabreytanna var um 4.6 km og tímaupplausn 1 dagur. Áhrif þess að minnka bæði svæðis –og tímaupplausn voru einnig könnuð. Tengsl gervitunglabreyta og veiðistaða makríls voru könnuð með tvíkostadreifðu GAM-líkani (Generalized Additive Model). Háða breytan var veiðistaðsetningar sem voru skilgreindar sem veiddur makríll eða enginn veiddur makríll. Óháðar breytur voru gervitunglabreyturnar með mismunandi svæðis – og tímaupplausn. Sjö daga meðaltal ljóstillífunargeislunar var sú einstaka breyta sem skýrði best makrílveiðar. Það módel með fleiri en einni óháðri breytu sem skýrð best makrílveiðar var módel með eins eða sjö daga meðaltal fyrir ljóstillífunargeislun og yfirborðssjávarhita og sjö daga meðaltal fyrir full staðlaðan geislunarljóma endurkastaðs ljóss frá vatni. Minni svæðisupplausn hafði ekki mikil áhrif á hæfileika gervitunglabreytanna til að skýra makrílveiðar en minni tímaupplausn frá einum degi til sjö daga bætti hæfileika flestra breytanna. Gervitunglabreyturnar sem voru notaðar komu úr stórum gagnasöfnum sem eru unnin fyrir heiminn í heild sinni. Til að meta hversu árangursrík slík gagnasöfn eru við staðbundnar aðstæður eins og á Íslandsmiðum voru tengsl milli blaðgrænu sem mæld er í sjó á Íslandsmiðum og magn blaðgrænu sem mæld er með gervitunglum á sömu stöðum borin saman. Niðurstöður, sem byggðu á litlu úrtaki, sýndu sterka marktæka fylgni. Gervitunglabreyturnar bættu marktækt módel til að skýra staðsetningu makrílveiðistaða. Makrílveiðar voru árangursríkastar við yfirborðshita sjávar frá 7,5°C – 13°C þar sem styrkur sólarljóss var mikill og þar sem magn blaðgrænu var í meðallagi. Tærari sjór og einnig sjór þar sem mikið endurkast á sér stað hefur líka áhrif. Þessar niðurstöður gefa til kynna að makríll sem veiddur er á Íslandsmiðum sé meira háður sjón við fæðuöflun en hingað til hefur verið álitið. Flestar heimildir segja að makríllinn afli fæðunnar fyrst og fremst með því að sía hana

Modeling the spatial distribution of African buffalo (Syncerus caffer) in the Kruger National Park, South Africa

The population density of wildlife reservoirs contributes to disease transmission risk for domestic animals. The objective of this study was to model the African buffalo distribution of the Kruger National Park. A secondary objective was to collect field data to evaluate models and determine environmental predictors of buffalo detection. Spatial distribution models were created using buffalo census information and archived data from previous research. Field data were collected during the dry (August 2012) and wet (January 2013) seasons using a random walk design. The fit of the prediction models were assessed descriptively and formally by calculating the root mean square error (rMSE) of deviations from field observations. Logistic regression was used to estimate the effects of environmental variables on the detection of buffalo herds and linear regression was used to identify predictors of larger herd sizes. A zero-inflated Poisson model produced distributions that were most consistent with expected buffalo behavior. Field data confirmed that environmental factors including season (P = 0.008), vegetation type (P = 0.002), and vegetation density (P = 0.010) were significant predictors of buffalo detection. Bachelor herds were more likely to be detected in dense vegetation (P = 0.005) and during the wet season (P = 0.022) compared to the larger mixed-sex herds. Static distribution models for African buffalo can produce biologically reasonable results but environmental factors have significant effects and therefore could be used to improve model performance. Accurate distribution models are critical for the evaluation of disease risk and to model disease transmission

Probabilistic uncertainty in an interoperable framework

This thesis provides an interoperable language for quantifying uncertainty using probability theory. A general introduction to interoperability and uncertainty is given, with particular emphasis on the geospatial domain. Existing interoperable standards used within the geospatial sciences are reviewed, including Geography Markup Language (GML), Observations and Measurements (O&M) and the Web Processing Service (WPS) specifications. The importance of uncertainty in geospatial data is identified and probability theory is examined as a mechanism for quantifying these uncertainties. The Uncertainty Markup Language (UncertML) is presented as a solution to the lack of an interoperable standard for quantifying uncertainty. UncertML is capable of describing uncertainty using statistics, probability distributions or a series of realisations. The capabilities of UncertML are demonstrated through a series of XML examples. This thesis then provides a series of example use cases where UncertML is integrated with existing standards in a variety of applications. The Sensor Observation Service - a service for querying and retrieving sensor-observed data - is extended to provide a standardised method for quantifying the inherent uncertainties in sensor observations. The INTAMAP project demonstrates how UncertML can be used to aid uncertainty propagation using a WPS by allowing UncertML as input and output data. The flexibility of UncertML is demonstrated with an extension to the GML geometry schemas to allow positional uncertainty to be quantified. Further applications and developments of UncertML are discussed

Spatial and Temporal Analysis of Selected Birth Defects and Risk Factors in the Baton Rouge, Louisiana Metropolitan Statistical Area

About three percent of all infants are born with a congenital defect each year ranging from minor variants to life threatening abnormalities. The investigation and treatment of these problems is both costly and emotionally trying for all involved. Finding their origins is a complex process. Birth defects create the ultimate mystery in terms of trying to tease out the various influences created by the environment of both the infant and the mother. Two genetically different individuals are simultaneously affected both by their individual makeup and by the outside world impacting the air they breathe, the food they eat, and the various stressors both big and small that are part of the world they live in. The availability of birth certificate data allows researchers to begin the process of sorting out the factors linked with birth defects. This dissertation employs data from 2005 to 2008 for live births occurring in the Baton Rouge Metropolitan Statistical Area (MSA). Geographic Information Systems (GIS) mapping, cluster analysis, spatial-temporal analysis, geographically weighted regression, and multilevel modeling were employed for the purpose of producing a baseline picture of the area in regard to the locations of mothers giving birth to infants with birth defects, the types and rates of those birth defects, and their correlates. The Baton Rouge MSA proved to be typical in terms of rates of birth defects worldwide, however there were areas which exceeded expected overall rates and some clustering of certain types of defects. Heart defects and hypospadias rates were slightly above anticipated percentages predicted by The U.S. Centers for Disease Control and Prevention. Temporal analysis revealed increases in rates of several types of birth defects in 2006 and 2007 but there were not enough years to analyze these rates statistically. Analysis of correlates did not reveal any models which could be used to impact rates in the future. However, this project provides baseline data on types and rates of birth defects and information on the best locations for services to affected families along with multiple opportunities for possible preventative efforts and future investigations of this area

Assessment of earthquake-triggered landslides in Central Nepal

Landslides are recurrent in Nepal due to active tectonics, high precipitation, complex topography, geology, and land use practices. Reliable landslide susceptibility maps are crucial for effective disaster management. Ongoing research has improved landslide mapping approaches, while further efforts are needed to assess inventories and enhance susceptibility mapping methods. This thesis aims to evaluate the landslides caused by the Gorkha earthquake in 2015 and develop reliable landslide susceptibility maps using statistical and geospatial techniques. There are four main objectives: (i) proposing clustering-based sampling strategies to increase the efficiency of landslide susceptibility maps over random selection methods, (ii) identifying and delineating effective landslide mapping units, (iii) proposing an innovative framework for comparing inventories and their corresponding susceptibility maps, and (iv) implementing a methodology for landslide-specific susceptibility mapping. Firstly, a comprehensive Gorkha earthquake-induced landslide inventory was initially compiled, and six unsupervised clustering algorithms were employed to generate six distinct training datasets. An additional training dataset was also prepared using a randomised approach. Among the tested algorithms, the Expectation Maximization using the Gaussian Mixture Model (EM/GMM) demonstrated the highest accuracy, confirming the importance of prioritising clustering patterns for training landslide inventory datasets. Secondly, slope units were introduced as an effective mapping unit for assessing landslides, delineating 112,674 slope unit polygons over an approximately 43,000 km2 area in Central Nepal. This is the first instance of generating such comprehensive mapping and making it publicly accessible. Thirdly, a comparison of five post-Gorkha earthquake inventories and susceptibility was conducted, revealing similarities in causative factors and map performance but variations in spatial patterns. Lastly, a rockfall inventory along two significant highways was developed as a landslide-classified inventory, and the rockfall susceptibility was evaluated. A segment-wise map with a 1 to 5 scale indicating low to high susceptibility was published for public use. This thesis proposes new approaches to landslide inventory sampling and earthquake-triggered landslide assessment. It provides publicly accessible databases for Central Nepal's slope unit map and rockfall susceptibility along the major highways. These findings can benefit researchers, planners, and policymakers to enhance risk management practices by advancing landslide assessment, particularly for earthquake-induced landslides in Central Nepal