International Symposium on Spatial Data Quality 2017 - ISPRS Geospatial Week 2017
ISSDQ 2017 - Invited Speaker
Sept 19-20, 2017,Wuhan University, Wuhan, Hubei, China
Prof. Peter M. Atkinson is the dean of Faculty of Science and Technology at Lancaster University. The main focus of his research has been in spatial data science and spatial and spatio-temporal statistics applied to a range of environmental and epidemiological phenomena using remote sensing and other big data. Four of the most significant themes of his research group are in (i) remote sensing image downscaling and image fusion, based on explicit models of the space-time sampling framework, (ii) remote sensing of global changes in vegetation phenology and its climate drivers, (iii) spatial epidemiology of vector-borne disease transmission systems, including Trypanosomiasis and malaria, based on agent-based dynamic models and Bayesian mixed regression models, and (iv) spatial modeling of natural hazards and their impacts, including flood forecasting based on Kalman-filter variants, landslide susceptibility mapping based on mixed models and, with colleagues in Southampton, near-Earth object impact simulation based on Newtonian orbital dynamics and big data. He has published over 230 peer-reviewed articles on these topics in international scientific journals, his Thompson H-index is 56 in Google Scholar and 39 in WoS, and he has led multiple large grants and supervised over 50 PhD students. In addition, he has published one book, over 50 refereed book chapters, edited eight journal special issues, and edited eight books. He is Associate Editor for Computers and Geosciences and sits on the editorial boards of Geographical Analysis, Spatial Statistics, the International Journal of Applied Earth Observation and Geoinformation, and Environmental Informatics.
Uncertainty in downscaling
Abstract:Downscaling is an important issue in remote sensing. It refers to the process of producing a finer spatial resolution image than that of the input image. Two types of downscaling issues can be identified based on whether spatial continua or land cover categories (refers to sub-pixel mapping or super-resolution mapping) are predicted. Downscaling is an ill-posed, inverse problem, and some of the required fine spatial resolution information cannot be recovered in some cases, especially for heterogeneous landscapes. However, unavoidably there exists uncertainty in downscaling. In this talk, for both issues (i.e., predicting spatial continua and land cover categories), we introduce an index based on the concept of information gain to evaluate the downscaling predictions. The index can be a complement to the existing ones based on point accuracy, especially when the fine spatial resolution reference is unavailable for quantitative assessment. We also discuss the uncertainty introduced by the point spread function (PSF) effect.
Prof.Jixian ZHANG is Director of National Quality inspection and Testing Center for Surveying and Mapping Products and Editor-in-Chief of International Journal of Image and Data Fusion (IJIDF). He was President of Chinese Academy of Surveying and Mapping (CASM) from July 2006 to February 2016. Prof.Jixian ZHANG has built up his reputation in China through many years’ research work in the fields of photogrammetry and remote sensing, land surveying, cadastral surveying and mapping, and management of academic programs and organizations, and made remarkable contributions to the development of the surveying profession in China. He published over 200 articles in reputation journals and 7 books, won five prestigious National Awards of Science and Technology Progress of China and one Geospatial World Excellence Award. Prof.Jixian ZHANG has many experiences in the international and domestic academic and professional communities and also been interested in international professional activities. He also has rich experiences in international academic exchanges, including his past efforts in organizing 13 international conferences/workshops and making 50+ international invited presentations in surveying, mapping and geoinformation.
Spatial precision and statistical precision: can we have the best of both worlds?
Abstract:Modern digital technology enables us to collect, store and map social and environmental data in fine geographical detail. Such spatial precision is of considerable benefit in helping us to observe and track change at the small area level in for example disease risk, crime risk and environmental risks. This in turn has the potential to enable us to better understand the processes at work and to respond to threats to life and well-being.However to be able to make the best use of such spatially fine-grained data we often need to be able to obtain reliable small area level estimates of attribute properties. But in many instances estimates for small geographical areas suffer from the “small number problem” and a lack of statistical precision. Estimator error in such circumstances may be unacceptably large thereby undermining the benefits that can be derived from such data.I shall look at ways geographical information scientists and spatial statisticians have tried to tackle this problem of spatial data quality in order to have the “best of both worlds” – spatial precision and statistical precision.
Prof. Bob Haining has been the professor of Human Geography at Department of Geography, University of Cambridge since 2000. He is the co-founder of the Spatial Econometrics Association and is an associate editor of several prominent Geographical and interdisciplinary journals including the Journal of Geographical Systems, Geographical Analysis, Spatial and Spatio-Temporal Epidemiology and Computational Statistics. For more than thirty years he has made significant contributions to spatial analysis with applications in the areas of spatial epidemiology and health services research, the geography of crime and economic geography. He has published widely, including two popular books: Spatial Data Analysis in the Social and Environmental Sciences (1990) and Spatial Data Analysis: Theory and methods (2003), and articles in over 20 different academic journals in areas that include geography, statistics, economics and epidemiology.
National Surveying Mapping and Geoinformation Quality Management and Supervision System
Abstract:This presentation offers an insight into quality management work of surveying and mapping products in China background of the national laws and department regulations. Combining the affect of strategy of “National Quality Infrastructure” in operation, the presentation gives you an overview of the basis, implement and impact of national quality supervision work in China.