Naru is interested in the quantitative appreciation of the urban and built environments with particular focus on its spatial-temporal changes. He currently pursues three research directions: (1) Spatial Analysis & Simulations (Developing spatial analytical methods for urban growth modelling. Developing quantitative methods on spatial optimisation, spatial tessellation, network analysis, and spatial-temporal analysis); (2) GIScience & Geo-visualisation (Developing GIS tools and spatial-temporal 3D GIS models for visualising urban growth, GIS applications in healthcare, archaeology and other social-science subjects); (3) Urban & Regional Analysis (Identifying the underlying mechanism of urban growth dynamics, urban sprawl and urban life cycle. Applying spatial analytical methods and simulations to understand the spatial-temporal transition of urban land uses, urban social clusters, and virtual cities and cyberspaces).
B. Engineering, Department of Urban Engineering, University of Tokyo, Japan (1995).
M. Engineering, Department of Urban Engineering, University of Tokyo, Japan (1997).
Ph.D. (Architecture), Bartlett School of Planning, University College London (2005).
Research Associate, Centre for Advanced Spatial Analysis, University College London (2000-2003).
Assistant Professor, Department of Geography, The State University of New York at Buffalo (2003-2009).
Lecturer, School of City and Regional Planning, Cardiff University (2009 onwards).
Awards and Prizes
Sheikh Dr Sultan Bin Mohamed Al Qassimi International Prize for the Best International Paper in Urban Planning in the Sixth Sharjah Urban Planning Symposium (with P. Torrens), Role: Lead author.
A spatial-temporal 3D GIS model (with L. Yin; funded by SUNY Buffalo & Japan Architecture Construction Information Center): A spatial-temporal 3D city model can support the process of urban planning & management as well as the spatial analysis of land-use changes. We have developed a proto-type GIS model with an innovative spatial-temporal data query system that allows us to construct a 3D urban scene on the fly (Shiode & Yin 2008).
Using GIS for perinatal healthcare (with L. Caley; funded by SUNY Buffalo): This project examines the causal relationship between poor birth outcomes (low birth weight, pre-term delivery) and social-demographic factors. GIS analysis indicates that low income status, low educational attainment, and mother’s age are among the most significant risk factors. A GIS-based decision-support system has been developed to help local healthcare experts alert their respective community and to improve the quality of healthcare consultation offered at local facilities (Caley, Shiode & Shelton 2008).
ABM of the greenbelt in Seoul Metropolitan Region (with Daejong Kim: funded by Korean Research Institute for Human Settlements): Suburbanisation of Seoul was previously controlled by the enforcement of a greenbelt zone, but this is rapidly changing with the revision of their land-use policy. We were commissioned to assess the effectiveness of ABM for simulating such changes. The project is expected to help us evaluate the impact of development in the greenbelt zone under different scenarios.
Comparison of real and virtual cities (with P. Torrens): I have conducted a series of comparative studies on the growth pattern of a real city and its virtual counterpart (Shiode & Torrens 2003, 2008). Empirical analysis on the changes in their fractal dimension, angular frequency and other imagery analysis techniques shows that the two cities, while comparable in their growth rate, have distinctively different growth patterns.
Network-based Variable-Distance Clumping Method (NTVCM) (with S. Shiode; funded by Japan Society for the Promotion of Science): This study proposes a new method for point-pattern analysis that extracts statistically significant clumps among point distribution on a network using a variable clump radius (Shiode & Shiode 2008b). It is a distance-based spatial-analytical method that uses the network Delaunay diagrams and the network minimum spanning tree newly extended for this study. A GIS tool has been also developed for computing NTVCM. Empirical study on the agglomeration of urban facilities reveal a notable difference in the performance of NTVCM and the conventional planar methods, confirming the effectiveness of NTVCM to provide a more accurate description of point agglomerations along networks.