Li, Y. (2015) Measuring and assessing the impacts of London 2012. The London Olympics and Urban Development: The Mega-Event City, Routledge: 35-47 

Li, Y. and Brimicombe, A.J. (2015) "A New Approach on Rapid Appraisal of Green Roof Potential in Urban Area" LIDAR Magazine, Vol 5 Issue 5 (July 2015): 55-57

We have successfully used LiDAR data to assess the green roof potential during our work in the project of TURaS. TURaS (Transitioning towards Urban Resilience and Sustainability) is funded by the Seventh Framework Programme of European Union. Our work in this project focuses on green roof development in London. This application has been eventually evolved to a straightforward and effective approach which can be used in green infrastructure development around urban area. Green roofs are important for resilient urban communities as they assist runoff attenuation, promote evapotranspiration, help improve air quality, result in energy savings, and provide recreational spaces. There has been very little work on rapid appraisal of green roof potential which this study addresses using LiDAR data. The London case study shows that LiDAR can cost-effectively classify roof geometry for large areas according to green roof design criteria as an input to the planning process

Brimicombe, A.J. (2009) GIS, Environmental Modelling and Engineering (2nd Edition). CRC Press, Boca Raton, FL, USA.

Lawal, O.; Brimicombe, A.J. and Li, Y. (2008) "Monitoring dynamics of urban landscape using spatial morphological indices: a case study of the Thames Gateway area". Proceedings, Advances in Computing & Technology 2008, London: 154-164

Li, Y. (2007) "Control of spatial discretisation in coastal oil spill modelling" International Journal of Applied Earth Observation and Geoinformation, 9: 392-402

Spatial discretisation plays an important role in many numerical environmental models. This paper studies the control of spatial discretisation in coastal oil spill modelling with a view to assure the quality of modelling outputs for given spatial data inputs. Spatial data analysis techniques are effective for investigating and improving the spatial discretisation in different phases of the modelling. Proposed methods are implemented and tested with experimental models. A new "automatic search" method based on GIS zone design principles is shown to significantly improve discretisation of bathymetric data and hydrodynamic modelling outputs. The concepts and methods developed in the study are expected to have general relevance for a range of applications in numerical environmental modelling

Lawal, O.; Gaiser, T. and Nuga, B. (2007) "Estimation of potential soil losses on a regional scale. A case of Abomey-Bohicon Region" Agricultural Journal, 2(1): 1-8

Li, Y. (2006) "Spatial data quality analysis with Agent technologies" Proceedings GISRUK2006, Nottingham: 250-254

Agent technologies have attracted increasing attention from GIS research and applications. Intelligent agents have shown considerable potential for spatial data analysis. This paper proposes an agent-based solution for spatial data quality analysis. A collection of collaborating agents is constructed in a multi-agent framework. These remote intelligent agents can be shared as spatial data quality analysis tools over a network. The proposed solution therefore offers a decentralised, distributed and service-oriented system. It is expected to aid users in designing their own data quality test procedures for environmental simulation models

Nuga, B.O.; Akinbola, G.E.; Lawal, O.; Gaiser, T. and Herrmann, L. (2006) " Production of the 1:350,000 scale digital geologic map of old Imo State" EJEAFChe, 5(6), 2006

Li, Y. (2005) "Agent technologies for spatial data quality analysis in environmental modelling" Proceedings GeoComputation 05, Ann Arbor, Michigan (CD)

The study shows the potential of an agent-based Geo-data Quality Analysis engine. With this interoperable engine, spatial data quality analysis can be achieved in environmental modelling. Ultimately, in the longer term, through such agents, the Geo-data Quality Analysis engine would be distributed on the Internet to be used by the scientific and professional community

Brimicombe, A.J. (2003). GIS, Environmental Modelling and Engineering. Taylor & Francis, London

The significance of modelling in managing the environment is well recognised from scientific and engineering perspectives as well as in the political arena. Environmental concerns and issues of sustainability have permeated both public and private sectors, particularly the need to predict, assess and mitigate against adverse impacts that arise from continuing development and use of resources. Environmental modelling is an inherently spatial activity well suited to taking advantage of Geographical Information Systems (GIS) functionality whether this be modelling aspects of the environment within GIS or linked to external simulation models. In doing so, a number of issues become important: environmental models need to consider space-time processes often in three-dimensions whereas GIS are largely two-dimensional and static; environmental models are often computational simulations, as distinct from cartographic modelling and map algebra in GIS; what should be the degree of integration between GIS and environmental models on any project; how does uncertainty in spatial data from GIS and the choices of parameterisation in environmental models combine and propagate through analyses to affect outputs; by what means can decisions be made with uncertain information. These issues inevitably arise in every scientific and engineering project to model and manage our environment. Students need to be made aware of these issues. Practitioners should enrich their knowledge and skills in these areas. This book focuses on the modelling, rather than on data collection or visualisation - other books adequately cover these areas - and aims to develop critical users of both GIS and environmental models.

Li, Y. (2004) "Control of spatial discretisation in coastal oil spill modelling" Proceedings GISRUK2004, East Anglia: 64-68

Spatial discretisation is one effective mean of spatial data modelling. Data aggregation or division over space for census data, social or economic modelling has attracted much attention in GIS research. For environmental problems, the impact of environmental change also has a spatial dimension. In most environmental simulation modelling, the numerical computation or manipulation has to work with discretised spatial data rather than continuous data or point survey data. Spatial discretisation has therefore been widely used for numerical environmental modelling. Through spatial discretisation, a tessellation for numerical modelling is established to regroup spatial data to match specific criteria. The diversity and complexity of environmental modelling raises a number of interesting issues as well as demands for proper study. In coastal oil spill modelling, spatial discretisation is the basis for numerical computation and simulation. Discretisation is used to construct the modelling mesh for Finite Element or Finite Differential computation in hydrodynamic modelling. It will also generate the modelling grid for trajectory and fate simulations. Current de facto industry procedures for such discretisations are pragmatic. However in many cases, they lack quality control and depend on the modeller's experience. With spatial analysis techniques, this paper will study the control of spatial discretisation in coastal oil spill modelling with a view to assure the quality of modelling for given spatial data inputs.

Li, Y.; Grainger, A. et al. (2004) "Using GIS techniques to evaluate community sustainability in open forest lands in Sub-Saharan Africa" in Methodologies, Models and Instruments for Rural and Urban Development (ed. Dixon-Gough), Ashgate Publishing, Aldershot.

Community sustainability in villages in developing countries is a meaningful concept to their inhabitants, whose livelihoods heavily depend on renewable natural resources in the immediate vicinity. This Paper describes how the integrated use of optimization models and Geographic Information System (GIS) models can give insights into community sustainability. The case studies are carried out for villages in open forest lands of three African countries - Senegal, Tanzania and Uganda. GIS efficiently manage various data in this project and provide the basis for practical planning techniques.

Lawal, O.; Gaiser, T. and Stahr, K. (2004) "Effects of Land Use changes on sediment load in Zagbo River Catchment in Southern Benin: Deutscher Tropentag" October 2004, Berlin

Li. Y. and Brimicombe A.J. (2003) "A Spatial Data Quality Analysis Engine for Coastal Oil-Spill Modelling" Proceedings of Conference on Oil Spill, Oil Pollution and Remediation, Istanbul: 43-54

Oil-spill models play an important role in prevention, preparation, response and impact analysis for coastal oil-spill pollution. The reliability of an oil-spill model depends on the accuracy of spatial data entry, integration and computation. There are a wide range of data quality problems for both data and model operations. This paper presents research work for a newly-developed Geo-data Quality Analysis (GQA) engine, which is constructed as a tightly-coupled collection of GI tools. Off-the-shelf tools are intended to work together in an interoperable framework for a specified oil-spill model. It overcomes the lack of spatial data quality functionality in GIS software as well as the limitation of fully-integrated package. A conceptual prototype of GQA engine has been developed, which includes some basic analysis tools and also provides help in designing test procedures for the specific model. This prototype has been applied for the coastal oil-spill modelling and should be generally applicable with its flexible structure and compatible tools.

Li, Y. and Brimicombe, A.J. (2002) "Assessing the quality implications of accessing spatial data: the case for GIS and environmental modelling" Proceedings GISRUK 2002, Sheffield: 68-71

For the spatial sciences, the 1990s were a period of transition from data-poverty to data-richness. Digital spatial data sets have grown rapidly in scope, coverage and volume (Miller & Han, 2001). This state change has been facilitated by: improved technology and wider use of GPS, remote sensing and digital photogrammetry for data collection; the introduction of new technologies such as LiDAR and radar interferometry; the operation of Moore's Law resulting in increased computing power to process raw data coupled with the falling cost of data storage; the advent of data warehousing technologies; increasingly efficient ways of accessing and delivering data on-line. The technical advances in hardware, software and data have been so profound that their effect on the range of problems studied and the methodologies used have been fundamental. At the same time however, problems have arisen in identifying and locating relevant data and in evaluating choices of resolution, coverage, provenance, cost and conformance with the models to be used. Furthermore, for environmental models it may not be so much the characteristics of the raw data that are the most critical but their characteristics once converted, aggregated and implemented in the model. Given that a modelling task may access data from multiple sources, there is the added difficulty of assessing combined performance in relation to the implementation of the simulation such that outputs have fitness-for-use. Clearly front-end tools are required in order to resolve these data issues both prior to purchase and also in specifying new data collection. A conceptual prototype for Geo-Quality Analysis (GQA) engine is developed, which includes off-shelf quality analysis tools and experiment design program. Data-richness has lead to choice and the need to evaluate that choice from the outset in terms of their implications for fitness-for-use in environmental modelling. The application of a GQA prototype in coastal oil spill modelling has shown the feasibility of doing this.

Li, Y.; Brimicombe, A.J. and Ralphs, M. (2000) "Spatial data quality and sensitivity analysis in GIS and environmental modelling: The case of coastal oil spills" Computers, Environment & Urban Systems 24: 95-108

Integration with environmental modelling, concern for spatial data quality issues and the rise of geocomputation paradigm have been three important areas of GIS research. In this paper they are brought together in the context of coastal oil spill modelling. Spatial data quality analyses of data and model elements for output sensitivity and error propagation, highlight the need to revise the coupling strategies of GIS and environmental modelling to include a geo-data analysis (GQA) engine. The implementation of comprehensive geospatial data quality analysis procedures within existing GIS appears unlikely. Nevertheless, as the complexity of data sets and the modelling capability of computer systems increase, the need to address the quality of both data and models is increasingly important. With growing availability of proprietary and public domain software suitable for spatial data quality analysis, GQA engines will evolve from the assembly of these tools external to GIS. GIS, environmental models and GQA will form a tightly-coupled modelling network, which, because of the importance of quality issues and the need for systematic testing, will see the dominant interaction between the GQA and the environmental modelling.

Li, Y.; Ralphs, M. and Brimicombe, A.J. (2000) "Error propagation for complex environmental modelling - the case of spatial data quality in coastal oil spill modelling" Proceedings Accuracy 2000: 409-16

Error propagation analysis can now offer a large amount of information about both spatial data inputs and modelling processes. This paper reports on the results of an error propagation study that takes coastal oil spill modelling as its operational context. In coastal oil spill modelling, any data error or uncertainty will be propagated through the sub-models because of the model configuration. There is also considerable scope for operationally-induced error or uncertainty. The analyses are carried out for spatial data inputs to the hydrodynamic model and initially focus on hydrodynamic modelling with knock-on error effects for trajectory and fate modelling. The paper also considers the use of error propagation techniques in assisting the user with the issue of algorithm choice in the oil spill modelling process. This paper gives the results of the experiments in full and provides an improved overall methodology for the study of error propagation for spatial data in environmental modelling.

Brimicombe, A.J. (1999) "Geographical information systems and environmental modelling for sustainable development" in Land Reform for Sustainable Development (ed. Dixon-Gough), Ashgate Publishing, Aldershot: 77-92

This paper focuses on land use planning issues of sustainable development in the face of escalating fatalities and economic losses due to naturally occurring environmental hazards. Incremental land use change and urban development can adversely effect the magnitude and frequency of geo-hazards resulting in greater risk for future generations. Hong Kong is cited as an example. Geographic Information Systems (GIS) are proving an effective tool in land use planning. In evaluating geo-hazards and in making decisions about the relative merits of structural and non-structural solutions, planning teams need to engage in environmental modelling. GIS can be used for 'spatial coexistence' and 'source-pathway characterization' modelling of geo-hazards. Particular emphasis is given to the construction of spatial decision support systems. GIS, however, are not a panacea. Some of the drawbacks in the context of land use planning for sustainable development are discussed.

Li, Y.; Brimicombe, A.J. and Ralphs, M. (1999) "Sensitivity to spatial data quality in numerical modelling coastal oil spill" in Proceedings GISRUK'99, Southampton, Vol. 7: 104-107

The simulation of coastal oil spills is an important form of environmental modelling due to the seriousness of both physical and social-economical impacts of such spills. Spatial data quality has come to be recognised as a critical issue in coastal oil spill modelling and is beginning to attract the attention of developers and users alike. The initial focus of this research has been on the influences of spatial data quality on the hydrodynamic modelling (which is treated as a grey box) with a consideration of knock-on effects for trajectory and fates modelling. A geocomputational approach has therefore been adopted whereby the use of data exploration software, a statistical package, geostatistics, fractal software, and visualisation software are coupled using GIS as a hub for data storage and handling. The sensitivity to a range of data qualities for shoreline representation, seabed bathymetry, sampling strategies, tidal data and so on can thus be systematically researched. A synthetic modelling approach is consequently being employed for the next stage of the research and for the spatial data quality tests presented in this paper. In this way the data in the experiments can be carefully and systematically controlled. Error propagation analysis offers much more information than error measurement. An overall model is derived for the spatial data quality propagation given that the hydrodynamic model are "grey box". Error propagation techniques, such as Monte Carlo method, are suitable for the complex dynamic-distribute models utilised in hydrodynamic modelling. By studying the effects of spatial data quality on numerical modelling of coastal oil spills, optimum strategies for managing and improving the spatial data quality using appropriate surveying techniques can be explored.

Chen, X.H.; Brimicombe, A.J.; Whiting, B. and Wheeler, C. (1998) "Enhancement of three-dimensional reservoir quality modelling by Geographic Information Systems" Geographical and Environmental Modelling 2: 125-139

Two- and three-dimensional (3D) mathematical modelling of the hydro-dynamics and pollution distributions in large water bodies is generally used on a grid with a relatively large cell size due to hardware limitations. Geographic information systems (GIS) have advanced capability for interpolation and visualization. Although GIS have been used for direct modelling, they are not designed for independent 3D modelling of large water bodies. This paper loosely couples a grid-based GIS with a 3D reservoir water quality model developed by the authors. Using this 3D model which is written in FORTRAN and run on a Pentium-133 computer, the 3D flow fields and distribution of Fe and Mn for the Arha Reservoir, China, are computed on a coarse grid of with a cell size of 50m square. Based on these results, a GIS grid-cell kriging function is used to obtain the distributions of Fe and Mn for the continuous water body on a finer grid of 25m cells. The results are compared with a rerun of the 3D reservoir quality model using the 25m grid, and this confirms the efficacy of kriging in the 3D reservoir quality model. Even smaller grids (2m square) of Fe and Mn concentrations in the area of water intake can be obtained by this GIS kriging which are suitable for a detailed study of the quality of the water supply. The use of kriging in a grid-based GIS in this paper greatly enhanced the 3D reservoir quality modelling.

Li, Y.; Brimicombe, A.J. and Ralphs, M. (1998) "Spatial data quality and coastal spill modelling" in Oil and Hydrocarbon Spills: Modelling, Analysis and Control, (eds. Garcia-Martinez & Brebbia), Computational Mechanics Publications, Southampton: 53-62

A growing number of numerical models have been applied to oil spill research, contingency planning and risk assessment. Whilst model quality has been a critical theme in their development and utilization, data quality in both quantitative and qualitative terms is also critical to the fitness of use of the outputs from the oil spill modelling process. Given the importance of such factors as shoreline and bathymetric representation, data resolution and method of interpolation in modelling coastal spills for example, it is essential that modelling of spatial data quality is applied to hydrodynamic, trajectory and fates modelling. This paper presents initial results of research in progress that will nevertheless emphasize to modeller and manager alike the practical issues of spatial data quality for coastal oil spill modelling. It is centred around a case study of Jiao Zhou Bay in the People's Republic of China. After summarizing the issue of spatial data quality in GIS, a taxonomy useful to oil spill modelling is presented. Some strategies for managing the spatial data quality in oil spill modelling are explored using GIS functionality for spatial data analysis and geostatistical modelling.

Tsui, P. and Brimicombe, A.J. (1998) "A conceptual framework of spatio-temporal process models for GIS: a planning perspective", Proceedings XX1 FIG Congress, Vol. 3: 383-397

Current development of GIS applications has been hindered by the lack of functionality to handle and analyse temporal geographical phenomena. A fundamental obstacle in developing temporal GIS is the absence of comprehensive and generalised conceptual models of spatio-temporal processes suitable for implementation in a GIS environment. This paper proposes a conceptual framework of spatio-temporal process models which can cover a wide range of temporal geographical phenomena and integrate with spatio-temporal data models for GIS. Finally, the use of this framework in modelling dynamic spatial processes in a planning perspective is illustrated.

Brimicombe, A.J. and Bartlett, J. (1996) "Linking geographic information systems with hydraulic simulation modelling for flood risk assessment: the Hong Kong approach" in GIS and Environmental Modelling: Progress and Research Issues (eds. Goodchild et al.), GIS World Inc.: 165-168 

Hong Kong's northern lowland basins have undergone substantial urban and sub-urban development over a 20 year period. This has been associated with worsening recurrent flood problems. An approach has been adopted whereby hydraulic modelling has been used in conjunction with geographic information systems (GIS) to produce 1:5,000 scale Basin Management Plans. GIS has a dual role: in data integration and quantification as an input to hydraulic modelling; in data interpolation, visualization and assessment of flood hazard and flood risk using the outputs from the hydraulic modelling. By using current land use and various development scenarios to be modelled over a range of rainstorm events, 'what if' decision support can be used in devising Basin Management Plans. Linking with hydraulic modelling requires a different approach to GIS data modelling than with the more traditional linkage with hydrological modelling only. The methodology developed in Hong Kong is presented as a case study.

Brimicombe, A.J. and Bartlett, J. (1993) "Spatial decision support in flood hazard and flood risk assessment: a Hong Kong case study" 3rd International Workshop on GIS, Beijing, Vol. 2: 173-182 

Hong Kong's northern New Territories has undergone rapid urban and sub-urban development which has worsened recurrent flooding problems. An approach has been adopted in Hong Kong whereby hydraulic modelling has been used in conjunction with geographic information systems to produce 1:5,000 scale Basin Management Plans. GIS has a dual role: in data integration and quantification as an input to hydraulic modelling; in data interpolation, visualisation and assessment of flood hazard and flood risk using the outputs from the hydraulic modelling. By using current land use and various development scenarios to be modelled over a range of rainstorm events, 'what if' decision support can be used in the design of the Basin Management Plans. Linking with hydraulic modelling requires a different GIS approach than with the more traditional linkage with hydrological modelling. The methodology developed in Hong Kong is presented as a case study.

Brimicombe, A.J. (1992) "Flood risk assessment using spatial decision support systems" Simulation 59: 379-380.

Urbanizing drainage basins are complex, dynamic man-environment systems. All too frequently the carefully balanced rural coexistence with the river and its floodplain, developed by trial and error over many years (often centuries), is severely disrupted by the geomorphic response to urban development within a drainage basin. Flooding can become both more frequent and more severe in terms of depth and duration. The consequences can be costly with disrupted livelihoods, damage to property and essential services and even loss of life. The integration of geographical information systems (GIS) and hydraulic simulation of flood events for different development scenarios is proving an effective tool in planning and operational management.

Centre for Geo-Information Studies

The Centre for Geo-Information Studies is an established research centre specialising specialise in all aspects of geo-information science.

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