From buildings to cities: techniques for the multi-scale analysis of urban form and function

The built environment is a significant factor in many urban processes, yet direct measures of built form are seldom used in geographical studies. Representation and analysis of urban form and function could provide new insights and improve the evidence base for research. So far progress has been slow due to limited data availability, computational demands, and a lack of methods to integrate built environment data with aggregate geographical analysis. Spatial data and computational improvements are overcoming some of these problems, but there remains a need for techniques to process and aggregate urban form data. Here we develop a Built Environment Model of urban function and dwelling type classifications for Greater London, based on detailed topographic and address-based data (sourced from Ordnance Survey MasterMap). The multi-scale approach allows the Built Environment Model to be viewed at fine-scales for local planning contexts, and at city-wide scales for aggregate geographical analysis, allowing an improved understanding of urban processes. This flexibility is illustrated in the two examples, that of urban function and residential type analysis, where both local-scale urban clustering and city-wide trends in density and agglomeration are shown. While we demonstrate the multi-scale Built Environment Model to be a viable approach, a number of accuracy issues are identified, including the limitations of 2D data, inaccuracies in commercial function data and problems with temporal attribution. These limitations currently restrict the more advanced applications of the Built Environment Model

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Sustainable urban development in practice:the SAVE concept

The need for sustainable development of the urban environment presents the research community with a number of challenges and opportunities. A considerable volume of research has been undertaken into the constituent parts of this complex problem and a number of tool kits and methodologies have been developed to enable and encourage the application of specific aspects of research in practice. However, there is limited evidence of the holistic integration of the body of knowledge arising from the research within real-life decision-making practices. In this paper we present an overview of the existing body of knowledge relating to sustainable development of the urban environment and propose a generic framework for its integration within current practices. This framework recognises the need to: understand social, economic, and environmental issues; understand the decision-making processes; provide a means of measurement, assessment, or valuation of the issues; provide analytical methods for the comparative assessment of complex data to enable an evaluation of strategies and design options and to communicate effectively throughout the process with a wide range of stakeholders. The components of a novel sustainability assessment, visualisation and enhancement (SAVE) framework, developed by the authors to ‘operationalise’ the body of knowledge are presented and justified. These include: decision-mapping methods to identify points of intervention; indicator identification and measurement approaches; appropriate mathematical and analytical tools and an interactive simulation and visualisation platform which integrates and communicates complex multivariate information to diverse stakeholder groups. We report on the application of the SAVE framework to a major urban development project and reflect on its current and potential impact on the development. Conclusions are also drawn about its general applicability

The National Transport Data Framework

Report by Professor Peter Landshoff (Cambridge University) and Professor John Polak (Imperial College London) on a project for the Department for Transport. emails: [email protected] [email protected] NTDF is designed to be a resource for data owners to deposit descriptions into a central catalogue, so that people can search for data and find data and understand their characteristics. The value of this is to individuals, to commercial organizations, and to public bodies. For example, services that provide better information to travellers will help to make their journey less stressful and persuade them to make more use of public transport. Transport operators need very diverse information to help them plan developments to their services: demographic, geographical, economic etc. And policy makers need a similar range of information to help them decide how to divide their budget and afterwards to evaluate how valuable it has been.This work was supported by the Department for Transport (DfT)

Climate change adaptation and vulnerability assessment of water resources systems in developing countries: a generalized framework and a feasibility study in Bangladesh

Water is the primary medium through which climate change influences the Earth’s ecosystems and therefore people’s livelihoods and wellbeing. Besides climatic change, current demographic trends, economic development and related land use changes have direct impact on increasing demand for freshwater resources. Taken together, the net effect of these supply and demand changes is affecting the vulnerability of water resources. The concept of ‘vulnerability’ is not straightforward as there is no universally accepted approach for assessing vulnerability. In this study, we review the evolution of approaches to vulnerability assessment related to water resources. From the current practices, we identify research gaps, and approaches to overcome these gaps a generalized assessment framework is developed. A feasibility study is then presented in the context of the Lower Brahmaputra River Basin (LBRB). The results of the feasibility study identify the current main constraints (e.g., lack of institutional coordination) and opportunities (e.g., adaptation) of LBRB. The results of this study can be helpful for innovative research and management initiatives and the described framework can be widely used as a guideline for the vulnerability assessment of water resources systems, particularly in developing countries

Quantitative Verification: Formal Guarantees for Timeliness, Reliability and Performance

Computerised systems appear in almost all aspects of our daily lives, often in safety-critical scenarios such as embedded control systems in cars and aircraft or medical devices such as pacemakers and sensors. We are thus increasingly reliant on these systems working correctly, despite often operating in unpredictable or unreliable environments. Designers of such devices need ways to guarantee that they will operate in a reliable and efficient manner. Quantitative verification is a technique for analysing quantitative aspects of a system's design, such as timeliness, reliability or performance. It applies formal methods, based on a rigorous analysis of a mathematical model of the system, to automatically prove certain precisely specified properties, e.g. ``the airbag will always deploy within 20 milliseconds after a crash'' or ``the probability of both sensors failing simultaneously is less than 0.001''. The ability to formally guarantee quantitative properties of this kind is beneficial across a wide range of application domains. For example, in safety-critical systems, it may be essential to establish credible bounds on the probability with which certain failures or combinations of failures can occur. In embedded control systems, it is often important to comply with strict constraints on timing or resources. More generally, being able to derive guarantees on precisely specified levels of performance or efficiency is a valuable tool in the design of, for example, wireless networking protocols, robotic systems or power management algorithms, to name but a few. This report gives a short introduction to quantitative verification, focusing in particular on a widely used technique called model checking, and its generalisation to the analysis of quantitative aspects of a system such as timing, probabilistic behaviour or resource usage. The intended audience is industrial designers and developers of systems such as those highlighted above who could benefit from the application of quantitative verification,but lack expertise in formal verification or modelling

Potential applications of geospatial information systems for planning and managing aged care services in Australia

[Abstract]: This paper discusses the potential applications of Geospatial Information Technology (GITs) to assist in planning and managing aged care programs in Australia. Aged care is complex due to the numbers of participants at all levels of including planning of services, investing in capacity, funding, providing services, auditing, monitoring quality, and in accessing and using facilities and services. There is a vast array of data spread across the entities that are joined to aged care. The decision-making process for investment in capacity and service provision might be aided by technology including GIT. This is also expected to assist in managing and analysing the vast amount of demographic, geographic, socio-economic and behavioral data that might indicate current and future demand for services the aged and frail-aged population. Mapping spatio-temporal changes in near real time can assist in the successful planning and management of aged care programs. Accurate information on the location of aged care services centres and mapping the special needs of clients and their service needs may assist in monitoring access to services and assist in identifying areas where there are logistic challenges for accessing services to meet needs. GIT can also identifying migrations of aged people and of the cohorts of the population who are likely to be the next wave of clients for aged care services. GITs include remote sensing, geographic information systems (GIS) and global positioning systems (GPS) technologies, which can be used to develop a user friendly digital system for monitoring, evaluating and planning aged care and community care in Australia. Whilst remote sensing data can provide current spatiotemporal inventory of features such as locations of carer services, infrastructure, on a consistent and continuous coordinate system, a GIS can assist in storing, cross analysing, modeling and mapping of spatial data pertaining to the needs of the older people. GITs can assist in the development of a single one-stop digital database which will prove a better model for managing aged care in Australia. GIT will also be a component of technologies such as activity monitors to provide tracking functionality. This will assist in tracking dementia sufferers who may be prone to wandering and be exposed to risk