A Bayesian Dynamic Spatio‐Temporal Interaction Model: An Application to Prostate Cancer Incidence

Kim, Hoon; Oleson, Jacob

doi:10.1111/j.0016-7363.2007.00714.x

Cited by 16 publications

(18 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Under the ICAR specification, the mean of s i and also δ i is given by the mean of neighboring values of s i and δ i , respectively, where neighboring areas are defined as those that share one or more common vertices (also referred to as first‐order queen contiguity). In the absence of known information regarding the spatial structure of the phenomenon (as explained in the context of malaria in de Castro and Singer ()), this specification is common in geographical studies (Kim and Oleson ; Rogerson and Kedron ). The ICAR specification also captures spatial structure in risks due to time effect and unmeasured factors that have spatial structure, but has limitations that are beyond the scope of this research, particularly when areas have different sizes and shapes and are arranged irregularly (Goovaerts ).…”

Section: Methodsmentioning

confidence: 99%

Analyzing Hotspots of Crime Using aBayesian Spatiotemporal Modeling Approach: A Case Study of Violent Crime in theGreaterTorontoArea

Law

Quick

Chan

2014

Geographical Analysis

View full text Add to dashboard Cite

Conventional methods used to identify crime hotspots at the small‐area scale are frequentist and employ data for one time period. Methodologically, these approaches are limited by an inability to overcome the small number problem, which occurs in spatiotemporal analysis at the small‐area level when crime and population counts for areas are low. The small number problem may lead to unstable risk estimates and unreliable results. Also, conventional approaches use only one data observation per area, providing limited information about the temporal processes influencing hotspots and how law enforcement resources should be allocated to manage crime change. Examining violent crime in the Regional Municipality of York, Ontario, for 2006 and 2007, this research illustrates a Bayesian spatiotemporal modeling approach that analyzes crime trend and identifies hotspots while addressing the small number problem and overcoming limitations of conventional frequentist methods. Specifically, this research tests for an overall trend of violent crime for the study region, determines area‐specific violent crime trends for small‐area units, and identifies hotspots based on crime trend from 2006 to 2007. Overall violent crime trend was found to be insignificant despite increasing area‐specific trends in the north and decreasing area‐specific trends in the southeast. Posterior probabilities of area‐specific trends greater than zero were mapped to identify hotspots, highlighting hotspots in the north of the study region. We discuss the conceptual differences between this Bayesian spatiotemporal method and conventional frequentist approaches as well as the effectiveness of this Bayesian spatiotemporal approach for identifying hotspots from a law enforcement perspective.

show abstract

Section: Methodsmentioning

confidence: 99%

Analyzing Hotspots of Crime Using aBayesian Spatiotemporal Modeling Approach: A Case Study of Violent Crime in theGreaterTorontoArea

Law

Quick

Chan

2014

Geographical Analysis

View full text Add to dashboard Cite

show abstract

“…Two examples of a more comprehensive model are the recently developed spatial panel approach (Elhorst, Blien, and Wolf 2007; Jiwattanakulpaisarn et al. 2009) and the Bayesian spatio‐temporal interaction model (Kim and Oleson 2008). These two models are capable of considering a wide range of variables as well as spatial and temporal influences, and they could be a starting point for developing a more comprehensive statistical model.…”

mentioning

confidence: 99%

An Integrated Framework of Population Change: Influential Factors, Spatial Dynamics, and Temporal Variation

Chi

Ventura

2011

Growth and Change

View full text Add to dashboard Cite

Population change has been studied in the fields of human ecology, population geography, environmental sociology, transportation planning, and regional economics, which make unique contributions to theorizing and modeling population change. Drawing upon this diverse literature, we develop an integrated framework for understanding population change in this review paper. The proposed framework is composed of five putative influences (demographic characteristics, socioeconomic conditions, transportation accessibility, natural amenities, and land development) analyzed across three dimensions-these influential factors of population change conditioned by spatial dynamics and temporal variation. We also proposed a practical procedure for tackling the complexity between population change and influential factors. This integrated framework has potential to complement existing population research approaches employed within individual disciplines. P opulation growth (or decline) is an important pursuit in the fields of demography, human ecology, population geography, environmental sociology, transportation planning, and regional economics. These fields adopt different theories to explain, and employ different approaches to model, population change. For example, human ecologists model population change within a holistic human-society-environment framework (Poston and Frisbie 2006). Population geographers are interested in spatial variations of population distribution, density, composition, and growth (Bailey 2005). Regional scientists and transportation planners study population via land use patterns (Boarnet 1997;Cervero 2003). Environmental planners focus on predictions of population change and

show abstract

“…Two examples include the recently developed spatial panel approach [47] and the Bayesian spatiotemporal interaction model [48]. These two models are capable of considering a wide range of variables as well as spatial and temporal influences.…”

Section: Future Researchmentioning

confidence: 99%

Population Change and Its Driving Factors in Rural, Suburban, and Urban Areas of Wisconsin, USA, 1970–2000

Chi

Ventura

2011

International Journal of Population Research

View full text Add to dashboard Cite

Population growth (or decline) is influenced by many factors that fall into the broad realms of demographic characteristics, socioeconomic conditions, transportation infrastructure, natural amenities, and land use and development across space and time. This paper adopts an integrated spatial regression approach to investigate the spatial and temporal variations of these factors' effects on population change. Specifically, we conduct the analysis at the minor civil division level in Wisconsin, USA, from 1970 to 2000. The results suggest that the factors have varying effects on population change over time and across rural, suburban, and urban areas. Their effects depend upon the general trend of population redistribution processes, local dynamics, and areal characteristics. Overall, a systematic examination of population change should consider a variety of factors, temporal and spatial variation of their effects, and spatial spillover effects. The examination should have the flexibility to identify and incorporate influential factors at a given point in time and space, not to adhere to a single set of drivers in all circumstances. The findings have important implications for population predictions used for local and regional planning.

show abstract

A Bayesian Dynamic Spatio‐Temporal Interaction Model: An Application to Prostate Cancer Incidence

Cited by 16 publications

References 23 publications

Analyzing Hotspots of Crime Using aBayesian Spatiotemporal Modeling Approach: A Case Study of Violent Crime in theGreaterTorontoArea

Analyzing Hotspots of Crime Using aBayesian Spatiotemporal Modeling Approach: A Case Study of Violent Crime in theGreaterTorontoArea

An Integrated Framework of Population Change: Influential Factors, Spatial Dynamics, and Temporal Variation

Population Change and Its Driving Factors in Rural, Suburban, and Urban Areas of Wisconsin, USA, 1970–2000

Contact Info

Product

Resources

About