Tomoki Nakaya

Visualising Crime Clusters in a Space-time Cube: An Exploratory Data-analysis Approach Using Space-time Kernel Density Estimation and Scan Statistics

For an effective interpretation of spatio-temporal patterns of crime clusters/hotspots, we explore the possibility of three-dimensional mapping of crime events in a space-time cube with the aid of space-time variants of kernel density estimation and scan statistics. Using the crime occurrence dataset of snatch-and-run offences in Kyoto City from 2003 to 2004, we confirm that the proposed methodology enables simultaneous visualisation of the geographical extent and duration of crime clusters, by which stable and transient space-time crime clusters can be intuitively differentiated. Also, the combined use of the two statistical techniques revealed temporal inter-cluster associations showing that transient clusters alternatively appeared in a pair of hotspot regions, suggesting a new type of “displacement” phenomenon of crime. Highlighting the complementary aspects of the two space-time statistical approaches, we conclude that combining these approaches in a space-time cube display is particularly valuable for a spatio-temporal exploratory data analysis of clusters to extract new knowledge of crime epidemiology from a data set of space-time crime events.

An Information Statistical Approach to the Modifiable Areal Unit Problem in Incidence Rate Maps

The modifiable areal unit problem becomes apparent when incidence rates are mapped on the basis of areal units. Although small units with high spatial accuracy can present unreliable rates, large spatial units may remove relevant geographical variation. Regarding mapping as a kind of statistical modelling, this author proposes a new methodology to select appropriate areal units using the Akaike information criterion and two search methods for an informative geographical aggregation in map construction. The optimal zoning of similarity is suitable for finding spatial anomalies but presents a biased overall pattern. An alternative approach is to cluster areal units according to explanatory variables: this shows clear spatial patterns of elderly mens mortality matching the ecological structure in the Tokyo metropolitan area.

Climatic controls on distribution of Fagus crenata forests in Japan

Abstract We used classification tree analysis to develop a climate-based distribution model for Fagus crenata forests in Japan. Four climatic variables judged likely to affect the distribution of the species (summer and winter precipitation, minimum temperature of the coldest month and Kiras warmth index) were chosen as independent variables for the model. Latitudinal and longitudinal information was also used to examine effects of spatial autocorrelation on the model. The climatic factors associated with the distribution of the forests were analysed using a classification tree to devise prediction rules. Predicted areas of high probability for forest occurrence lay mainly on the Sea of Japan side of northern Honshu and southern Hokkaido. This is consistent with actual forest distribution. Some areas with high predicted probabilities of F. crenata forest occurrence were beyond the current natural northern range limits of these forests. Since these areas were widely scattered, it was assumed that the species has been hindered from colonizing them due to dispersal limitations. Deviance-weighted scores, used to compare magnitudes of the contributions of predictor variables, revealed winter precipitation as the most influential factor, followed by the warmth index, the minimum temperature of the coldest month and summer precipitation. Attempts were made to generate ecological explanations for the effects of the four climatic factors on the distribution of F. crenata forests. Abbreviations: CA = Classification accuracy; DWS = Deviance weighted score; JMA = Japan Meteorological Agency; MER = Misclassification error rate; TMC = Minimum temperature of the coldest month; NSNE = National Survey on the Natural Environment; OE = Omission error; PRS = Summer precipitation; PRW = Winter precipitation; RMD = Residual Mean Deviance; WI = Warmth index. Nomenclature: Ohwi & Kitagawa (1992).

Perceived neighborhood environment and walking for specific purposes among elderly Japanese.

Background Recent research has revealed the importance of neighborhood environment as a determinant of physical activity. However, evidence among elderly adults is limited. This study examined the association between perceived neighborhood environment and walking for specific purposes among Japanese elderly adults. Methods This population-based, cross-sectional study enrolled 1921 participants (age: 65–74 years, men: 51.9%). Neighborhood environment (International Physical Activity Questionnaire Environmental Module) and walking for specific purposes (ie, transportation or recreation) were assessed by self-report. Multilevel logistic regression analyses with individuals at level 1 and neighborhoods at level 2 were conducted to examine the association between environment and walking, after adjustment for potential confounders. Results Access to exercise facilities, social environment, and aesthetics were associated with total neighborhood walking. Odds ratios (95% CI) were 1.23 (1.00–1.51), 1.39 (1.14–1.71), and 1.48 (1.21–1.81), respectively. Regarding walking for specific purposes, social environment and aesthetics were consistent correlates of both transportation walking and recreational walking. Environmental correlates differed by specific types of walking and by sex. Transportation walking significantly correlated with a greater variety of environmental attributes. Sex differences were observed, especially for transportation walking. Bicycle lanes, crime safety, traffic safety, aesthetics, and household motor vehicles were significant correlates among men, while access to shops, access to exercise facilities, and social environment were important among women. Conclusions Specific environment–walking associations differed by walking purpose and sex among elderly adults. Social environment and aesthetics were consistent correlates of both transportation walking and recreational walking. Improving these environmental features might be effective in promoting physical activity among elderly Japanese.

Probability distributions, vulnerability and sensitivity in Fagus crenata forests following predicted climate changes in Japan

Abstract Question: How much is the probability distribution of Fagus crenata forests predicted to change under a climate change scenario by the 2090s, and what are the potential impacts on these forests? What are the main factors inducing such changes? Location: The major islands of Japan. Methods: A predictive distribution model was developed with four climatic factors (summer precipitation, PRS; winter precipitation, PRW; minimum temperature of the coldest month, TMC; and warmth index, WI) and five non-climatic factors (topography, surface geology, soil, slope aspect and inclination). A climate change scenario was applied to the model. Results: Areas with high probability (> 0.5) were predicted to decrease by 91 %, retreating from the southwest, shrinking in central regions, and expanding northeastwards beyond their current northern limits. A vulnerability index (the reciprocal of the predicted probability) suggests that Kyushu, Shikoku, the Pacific Ocean side of Honshu and southwest Hokkaido will have high numbers of many vulnerable F. crenata forests. The forests with high negative sensitivity indices (the difference between simulated probabilities of occurrence under current and predicted climates) mainly occur in southwest Hokkaido and the Sea of Japan side of northern Honshu. Conclusion: F. crenata forest distributions may retreat from some islands due to a high WI. The predicted northeastward shift in northern Hokkaido is associated with increased TMC and PRS. High vulnerability and negative sensitivity of the forests in southern Hokkaido are due to increased WI. Abbreviations: CCSR/NIES = Centre for Climate System Research / National Institute for Environmental Studies; DWS = Deviance-weighted score; GCM = Global Climate Model; IPCC = Intergovernmental Panel on Climate Change; JMA = Japan Meteorological Agency; MER = Misclassification error rate; NSNE = National Survey on the Natural Environment; PRS = Summer precipitation; PRW = Winter precipitation; TMC = Minimum temperatuire coldest month; WI = Warmth index. Nomenclature: Ohwi & Kitagawa (1992).

Does walkable mean sociable? Neighborhood determinants of social capital among older adults in Japan.

Why are some communities more cohesive than others? The answer to the puzzle has two parts: (a) due to variations in the attributes of residents, and/or (b) due to variations in the attributes of places. However, few studies have sought to examine the community-level determinants of social capital. In the present study, we examined the associations between social capital and different area characteristics: (1) neighborhood walkability, (2) date of community settlement, and (3) degree of urbanization. We based our analysis on 9414 respondents from the Aichi Gerontological Evaluation Study (AGES), conducted in 2003. No significant positive association was found between the walkability score and any of the social capital indices. In contrast, community age and degree of urbanization were associated with many of the social capital indicators, even after controlling for characteristics of the residents. Community social capital thus appears to be more consistently linked to the broader historical and geographic contexts of neighborhoods, rather than to the proximal built environment (as measured by walkability).

Local Spatial Interaction Modelling Based on the Geographically Weighted Regression Approach

One of the recent major trends in spatial analysis is local modelling by which spatial analysts examine local properties in geographical phenomena (Fotheringham, 1997). Indeed, spatial processes tend to vary over space due to different geographical contexts so that spatial non-stationarity emerges (Jones III and Hanham, 1995). In such cases, global models that postulate universally acceptable properties fail to capture the real phenomena under study. We could say that inferences of local incidence rates in disease mapping are the simplest form of local modelling (Openshaw et al.,1987, Nakaya, 2000). As for more complicated association analyses, Casetti’s (1972) expansion method is popular to model explicitly the property of non-stationarity in regression analysis (e.g. Casetti, 1990). According to the method, we can specify geographical drifts of regression parameters by polynomial or harmonic expansion series of locational variables. Recently, the Newcastle school (Brunsdon et al., 1996, Fotheringham et al.,1998) has developed a more generalised local regression methodology, called geographically weighted regression (GWR). The approach estimates local regression coefficients with a moving weighting kernel.

Socio-economic status and self-rated health in East Asia: a comparison of China, Japan, South Korea and Taiwan

BACKGROUND Few cross-national studies have compared the relationship between socio-economic status (SES) and health among East Asian countries. This study elucidates the relationship between SES and self-rated health (SRH) in four societies of East Asia: China, Japan, South Korea and Taiwan. METHODS We used the data from the East Asian Social Survey 2006, which consists of nationally representative samples from each of the four countries. Logistic regression analysis of SRH was performed using four standardized SES indices (income, education, occupation and class identification) as explanatory variables to compare the degree of association of each SES index with SRH. RESULTS A total of 8120 respondents in the age range of 20-69 years were analysed. Overall, social gradients in health were observed in the East Asian societies. In China, South Korea and Taiwan, three of the four SES indices showed a statistically significant association for both male and female groups. In Japan, except class identification, no other SES index showed a significant relationship with SRH. With regard to the differences between the SES indices, class identification exhibited the strongest association with SRH, while occupational class displayed the weakest association. CONCLUSION Our study results indicate that Japan has low levels of health inequality compared to other East Asian countries. Furthermore, an index of occupational classes may be insufficient to explain health inequalities in East Asia.

Combining microsimulation and spatial interaction models for retail location analysis

Although the disaggregation of consumers is crucial in understanding the fragmented markets that are dominant in many developed countries, it is not always straightforward to carry out such disaggregation within conventional retail modelling frameworks due to the limitations of data. In particular, consumer grouping based on sampled data is not assured to link with the other statistics that are vital in estimating sampling biases and missing variables in the sampling survey. To overcome this difficulty, we propose a useful combination of spatial interaction modelling and microsimulation approaches for the reliable estimation of retail interactions based on a sample survey of consumer behaviour being linked with other areal statistics. We demonstrate this approach by building an operational retail interaction model to estimate expenditure flows from households to retail stores in a local city in Japan, Kusatsu City.

Hierarchical destination choice and spatial interaction modelling: a simulation experiment

A simulation experiment is proposed and undertaken to extend our understanding of the role of the competing destinations spatial interaction model in capturing the effects of hierarchical destination choice. In doing so, we make explicit the linkage between spatial choice behaviour at different levels of a spatial hierarchy. We also make explicit the way in which decisions made at one level of the hierarchy can lead to biased measurements of behaviour at a lower one. This is shown when conventional spatial interaction models are calibrated with interaction data that result from hierarchical destination choice.