23 research outputs found
Dynamic spatial weight matrix and localised STARIMA for network modelling
Various statistical model specifications for describing spatiotemporal processes have been proposed over the years, including the space–time autoregressive integrated moving average (STARIMA) and its various extensions. These model specifications assume that the correlation in data can be adequately described by parameters that are globally fixed spatially and/or temporally. They are inadequate for cases in which the correlations among data are dynamic and heterogeneous, such as network data. The aim of this article is to describe autocorrelation in network data with a dynamic spatial weight matrix and a localized STARIMA model that captures the autocorrelation locally (heterogeneity) and dynamically (nonstationarity). The specification is tested with traffic data collected for central London. The result shows that the performance of estimation and prediction is improved compared with standard STARIMA models that are widely used for space–time modeling.
En los últimos años, se han propuesto diversas especificaciones de modelado estadístico para describir procesos espacio-temporales. Esto incluye el modelo espacio-temporal autorregresivo integrado de media móvil (STARIMA) y sus varios derivados. Estas especificaciones de modelo asumen que la correlación de los datos puede ser adecuadamente descrita por parámetros que se fijan a nivel global en el espacio y/o tiempo. Dichos parámetros son inadecuados para los casos en los que las correlaciones entre los datos son dinámicas y heterogéneas, como en el contexto de los datos de la red. El objetivo de este artículo es describir la autocorrelación en los datos de red con una matriz de ponderación espacial dinámica y un modelo STARIMA localizado (LSTARIMA) que captura la autocorrelación local (heterogeneidad) de forma dinámica (no estacionariedad). La especificación del modelo es evaluada con datos de tráfico recolectados en el centro de Londres. Los resultados demuestran que los rendimientos de estimación y predicción mejoran con el método propuesto en comparación con los modelos STARIMA estándar que son ampliamente utilizados para el modelado de espacio-temporal
Analyzing housing market dynamics and residential location choices concurrent with light-rail transit investment in Kitchener-Waterloo, Canada
Transit investment and transit-oriented development (TOD) have become predominant planning policies to manage growth and limit sprawl. Waterloo Region implemented a light-rail transit (LRT) system aiming to provide alternative transit options and shape urban communities. Meanwhile, as one of the most fast-growing urban areas, the region has experienced rapid growth in population and employment. The booming high-tech industries, the international immigrants and migrants from the Greater Toronto Area (GTA) have all contributed to the increasing attractiveness of the region and its changing demographics, which in turn have heavily shifted the housing markets in the region. The housing prices have risen dramatically since 2014 and reached a peak in 2017 when the average sales price increased by over twenty percent from 2016. These changes occurring in the region have motivated this thesis to investigate 1) How have different housing markets in the region reacted to the LRT investment? 2) How might the LRT investment have influenced the residential location choices of various households? 3) Who might hold strong preferences for living in the TOD area? This thesis addresses these questions through three empirical studies. The first study presents a spatio-temporal autoregressive multilevel model to better examine the relationship between housing characteristics, transit investment, and housing prices. The proposed model is expected to improve the purely spatial hedonic price modeling in three aspects: i) controlling for both the spatial and temporal relations on housing price determination, i.e., the dependence on “recent comparable sales”; ii) considering the nesting structure of housing in neighbourhoods; and iii) accounting for the neighbourhood-level spatial interactions. Using 68,258 housing transactions occurring in Kitchener-Waterloo (KW) during 2005-2018, this study finds the better performance of the proposed models and provides strong evidence of the three distinct effects that underly the price generating process. According to the preferred model results, this study finds a significant housing price increase in the central-transit corridor (CTC), compared to housing outside the CTC, while the impacts vary for different housing types at different stages of the LRT implementation process. The second study seeks to delineate the housing demand structure in the region during the LRT construction. To this end, this research conducted a housing survey in KW through 2016-2017 and obtained 357 complete responses from homebuyers. Based on the survey data, this study performs a second-stage demand analysis and reports heterogeneous preference estimates of different demographics for dwelling and neighborhood attributes. Household structure and age seem to be the major demand shifters. This study also finds that both couples without children and seniors aged 55 and over are more willing to pay for the CTC area. The third study aims to identify household groups holding different preferences for TOD. Based on the survey responses regarding the importance of TOD features in residential location choices, this study conducts a latent-class analysis (LCA) and finds that 36.2 percent of households (primarily couples with children and with medium income) in our sample show a strong desire for TOD features, including LRT access, bus access, walkability, ease to cycle, access to urban centre and access to open space, although they purchased outside the CTC. This indicates a possible undersupply of housing in the CTC for these families with children. Through further examination of their preferences for other housing attributes, this study finds the adequate living space, garage and school quality are more important to these households. This thesis provides updated knowledge on housing market dynamics, housing demand and TOD preferences, which may help inform housing policies in the region to provide home options for a wide range of households inside and outside the central transit corridor and thus create vibrant and complete communities
Deriving Space-Time Variograms from Space-Time Autoregressive (STAR) Model Specifications
Many geospatial science subdisciplines analyze variables that vary over both space and time. The space–time autoregressive (STAR) model is one specification formulated to describe such data. This paper summarizes STAR specifications that parallel geostatistical model specifications commonly used to describe space–time variation, with the goal of establishing synergies between these two modeling approaches. Resulting expressions for space–time correlograms derived from 1st-order STAR models are solved numerically, and then linked to appropriate space–time semivariogram models
Space-time statistical analysis of malaria morbidity incidence cases in Ghana: A geostatistical modelling approach
Malaria is one of the most prevalent and devastating health problems worldwide. It is a highly endemic disease in Ghana, which poses a major challenge to both the public health and socio-economic development of the country. Major factors accounting for this situation include variability in environmental conditions and lack of prevention services coupled with host of other socio-economic factors. Ghana’s National Malaria Control Programme (NMCP) risk assessment measures have been largely based on household surveys which provided inadequate data for accurate prediction of new incidence cases coupled with frequent incomplete monthly case reports. These raise concerns about annual estimates on the disease burden and also pose serious threats to efficient public health planning including the country’s quest of reducing malaria morbidity and mortality cases by 75% by 2015.
In this thesis, both geostatistical space-time models and time series seasonal autoregressive integrated moving average (SARIMA) predictive models have been studied and applied to the monthly malaria morbidity cases from both district and regional health facilities in Ghana. The study sought to explore the spatio-temporal distributions of the malaria morbidity incidence and to account for the potential influence of climate variability, with particular focus on producing monthly spatial maps, delimiting areas with high risk of morbidity. This was achieved by modelling the morbidity cases as incidence rates, being the number of new reported cases per 100,000 residents, which together with the climatic covariates were considered as realisations of random processes occurring in space and/or time.
The SARIMA models indicated an upward trend of morbidity incidence in the regions with strong seasonal variation which can be explained primarily by the effects of rainfall, temperature and relative humidity in the month preceding incidence of the disease as well as the morbidity incidence in the previous months. The various spacetime ordinary kriging (STOK) models showed varied spatial and temporal distributions of the morbidity incidence rates, which have increased and expanded across the country over the years. The space-time semivariogram models characterising the spatio-temporal continuity of the incidence rates indicated that the occurrence of the malaria morbidity was spatially and temporally correlated within spatial and temporal ranges varying between 30 and 250 km and 6 and 100 months, respectively. The predicted incidence rates were found to be heterogeneous with highly elevated risk at locations near the borders with neighbouring countries in the north and west as well as the central parts towards the east. The spatial maps showed transition of high risk areas from the north-west to the north-east parts with climatic variables contributing to the variations in the number of morbidity cases across the country. The morbidity incidence estimates were found to be higher during the wet season when temperatures were relatively low whilst low incidence rates were observed in the warm weather period during the dry seasons.
In conclusion, the study quantified the malaria morbidity burden in Ghana to produce evidence-based monthly morbidity maps, illustrating the risk patterns of the morbidity of the disease. Increased morbidity risk, delimiting the highest risk areas was also established. This statistical-based modelling approach is important as it allows shortterm prediction of the malaria morbidity incidence in specific regions and districts and also helps support efficient public health planning in the country
Spatial Analysis of Cluster Randomised Trials
Cluster randomised trials (CRTs) often use geographical areas as the unit of randomisation. Despite this, explicit consideration of the location and spatial distribution of observations is rare. In many trials, the location of participants will have little importance, however in some, especially against infectious diseases, spillover effects due to participants being located close together may affect trial results. This PhD takes a multidisciplinary approach to apply and evaluate spatial analysis methods in CRTs, furthering understanding of how spatial analysis can complement traditional evaluation of CRTs. I began by conducting a systematic review of CRTs that used spatial analysis techniques. I found only 10 published papers, most of which being supplementary analyses of the main trial. I then conducted a spatial analysis of an Oral Polio Vaccine (OPV) transmission household CRT. This provided additional insights into the underlying mechanism of polio transmission that support the global cessation of OPV and emphasises the difficulties of the global eradication of polio. Following this, I performed a spatial reanalysis of an insecticide-treated bed net CRT, applying approaches from the systematic review and a new method I developed called cluster reallocation to assess the presence and impact of spatial spillover in the trial. This analysis confirmed the previous estimate of intervention effect while showing evidence of a spillover effect. I carried out simulation studies to evaluate the impact of spillover and spatial effects on the standard CRT model and compared spatial regression to non-spatial models. These simulations focus on how to generate spatial spillover effects and the magnitude needed before spatial consideration becomes important to CRTs. I found that non-spatial CRT models are relatively robust to spatial effects and that the use of spatial models does not appear to improve upon the non-spatial model. The collective findings of this thesis highlight that standard CRT approaches are typically robust to small scale spillover effects and consideration of the spatial distribution of observations appears to provide little utility in the main analysis of a trial. Despite this, spatial methods can provide additional insights into the mechanism of interventions and are well suited to secondary analyses of CRTs, especially with the increasing collection of GPS data in CRTs
Spatial organisation of ecologically relevant high order flow properties and implications for river habitat assessment
PhDThe turbulent properties of flow in rivers are of fundamental importance to aquatic
organisms yet are rarely quantified during routine river habitat assessment surveys
or the design of restoration schemes due to their complex nature. This thesis uses a
detailed review of the literature to highlight the various ways in which plants and
animals modify the flow field, how this can deliver beneficial effects; and how
turbulence can also generate threats to growth and survival. The thesis then
presents the results from detailed field assessments of turbulence properties
undertaken on low, intermediate and high gradient rivers to advance scientific
understanding of the hydrodynamics of rivers and inform effective habitat
assessment and restoration. A reach-scale comparison across sites reveals spatial
variations in the relationships between turbulent parameters, emphasising the need
for direct measurement of turbulence properties, while a geomorphic unit scale
assessment suggests that variations in turbulence at the scale of individual
roughness elements, and/or within the same broad groupings of geomorphic units
(e.g. different types of pools) can have an important influence on hydraulic habitat.
The importance of small-scale flow obstructions is further emphasised through
analysis of the temporal dynamics of turbulence properties with changes in flow
stage and vegetation growth. The highest magnitude temporal changes in
turbulence properties were associated with individual boulders and vegetation
patches respectively, indicating flow intensification around these sub-geomorphic
unit scale features. Experimental research combining flow measurement with
underwater videography reveals that more sophisticated turbulence parameters
provide a better explanation of fish behaviour and habitat use under field conditions,
further supporting direct measurement of turbulent properties where possible. The
new insights into interactions between geomorphology, hydraulics and aquatic
organisms generated by this work offer opportunities for refining habitat assessment
and restoration design protocols to better integrate the important role of turbulence
in generating suitable physical habitat for aquatic organisms.SMART (Science for the MAnagement of Rivers and their Tidal systems) Joint Doctorate Erasmus Mundus
programme funded by the European Union
Handbook of Mathematical Geosciences
This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences
Seventh International Workshop on Simulation, 21-25 May, 2013, Department of Statistical Sciences, Unit of Rimini, University of Bologna, Italy. Book of Abstracts
Seventh International Workshop on Simulation, 21-25 May, 2013, Department of Statistical Sciences, Unit of Rimini, University of Bologna, Italy. Book of Abstract
Spatio-temporal forecasting of network data
In the digital age, data are collected in unprecedented volumes on a plethora of networks. These data provide opportunities to develop our understanding of network processes by allowing data to drive method, revealing new and often unexpected insights. To date, there has been extensive research into the structure and function of complex networks, but there is scope for improvement in modelling the spatio-temporal evolution of network processes in order to forecast future conditions. This thesis focusses on forecasting using data collected on road networks. Road traffic congestion is a serious and persistent problem in most major cities around the world, and it is the task of researchers and traffic engineers to make use of voluminous traffic data to help alleviate congestion. Recently, spatio-temporal models have been applied to traffic data, showing improvements over time series methods. Although progress has been made, challenges remain. Firstly, most existing methods perform well under typical conditions, but less well under atypical conditions. Secondly, existing spatio-temporal models have been applied to traffic data with high spatial resolution, and there has been little research into how to incorporate spatial information on spatially sparse sensor networks, where the dependency relationships between locations are uncertain. Thirdly, traffic data is characterised by high missing rates, and existing methods are generally poorly equipped to deal with this in a real time setting. In this thesis, a local online kernel ridge regression model is developed that addresses these three issues, with application to forecasting of travel times collected by automatic number plate recognition on London’s road network. The model parameters can vary spatially and temporally, allowing it to better model the time varying characteristics of traffic data, and to deal with abnormal traffic situations. Methods are defined for linking the spatially sparse sensor network to the physical road network, providing an improved representation of the spatial relationship between sensor locations. The incorporation of the spatio-temporal neighbourhood enables the model to forecast effectively under missing data. The proposed model outperforms a range of benchmark models at forecasting under normal conditions, and under various missing data scenarios