Program your city: Designing an urban integrated open data API

Cities accumulate and distribute vast sets of digital information. Many decision-making and planning processes in councils, local governments and organisations are based on both real-time and historical data. Until recently, only a small, carefully selected subset of this information has been released to the public – usually for specific purposes (e.g. train timetables, release of planning application through websites to name just a few). This situation is however changing rapidly. Regulatory frameworks, such as the Freedom of Information Legislation in the US, the UK, the European Union and many other countries guarantee public access to data held by the state. One of the results of this legislation and changing attitudes towards open data has been the widespread release of public information as part of recent Government 2.0 initiatives. This includes the creation of public data catalogues such as data.gov.au (U.S.), data.gov.uk (U.K.), data.gov.au (Australia) at federal government levels, and datasf.org (San Francisco) and data.london.gov.uk (London) at municipal levels. The release of this data has opened up the possibility of a wide range of future applications and services which are now the subject of intensified research efforts. Previous research endeavours have explored the creation of specialised tools to aid decision-making by urban citizens, councils and other stakeholders (Calabrese, Kloeckl & Ratti, 2008; Paulos, Honicky & Hooker, 2009). While these initiatives represent an important step towards open data, they too often result in mere collections of data repositories. Proprietary database formats and the lack of an open application programming interface (API) limit the full potential achievable by allowing these data sets to be cross-queried. Our research, presented in this paper, looks beyond the pure release of data. It is concerned with three essential questions: First, how can data from different sources be integrated into a consistent framework and made accessible? Second, how can ordinary citizens be supported in easily composing data from different sources in order to address their specific problems? Third, what are interfaces that make it easy for citizens to interact with data in an urban environment? How can data be accessed and collected

Introducing the transport impacts of E-business project

E-Business is expected to dramatically change the way business is conducted internationally, nationally, within states and at the local area level. Moreover, these changes are very likely to happen well within the planning time frames required for provision of transport infrastructure and services. This paper introduces a study to investigate: how will the transport task change; what will be affected; and how can the transport system respond? The work, a joint initiative of the National Transport Secretariat [NTS], and the CSIRO/QUT Built Environment Research Alliance, in collaboration with Queensland Department of Public Works, is intended to assist Australian business and government pro-actively address the transport issues arising from e-business. E-commerce is growing exponentially: worldwide e-commerce revenues are estimated to reach $US1300 billion by 2003. Growth in electronic communications within companies which, together with e-commerce between companies, B2B, or between business and consumers, B2C, make up e-business, is even more significant. The magnitude and speed of both these changes increase the urgency of planning, for their impacts provide forecasting challenges. A study approach is described to consider direct impacts from e-business on Australian transport, derived demand for travel or delivery as other businesses prosper and transport related constraints to e-business in the next five to ten years. Base line information collection: includes a review of literature and information about trends in e-business then transport and e-Business; interviews with stakeholder/experts in planning agencies, e-business companies and transport/logistics firms across Australia; appraisal of data available for on-going assessment; and contact with international experts. Trends and impact assessment: National trends will be estimated and very important impacts or very urgent impacts selected via a ranking and rating process. Transport related productivity gains will be assessed; trends in regional Australia and international trends and experience will be reported with a final assessment of opportunities and threats for Australia. Outputs: A series of working papers and final reports; a user friendly database of contacts, publications and data availability information for update; a research framework for identification of policy and planning levers to maximise benefits to Australia from national and global e-business activity

Tourism and the smartphone app: capabilities, emerging practice and scope in the travel domain.

Based on its advanced computing capabilities and ubiquity, the smartphone has rapidly been adopted as a tourism travel tool.With a growing number of users and a wide varietyof applications emerging, the smartphone is fundamentally altering our current use and understanding of the transport network and tourism travel. Based on a review of smartphone apps, this article evaluates the current functionalities used in the domestic tourism travel domain and highlights where the next major developments lie. Then, at a more conceptual level, the article analyses how the smartphone mediates tourism travel and the role it might play in more collaborative and dynamic travel decisions to facilitate sustainable travel. Some emerging research challenges are discussed

Deriving Supply-side Variables to Extend Geodemographic Classification

The traditional proprietary geodemographic information systems that are on the market today use well-established methodologies. Demographic indicators are selected as a proxy for affluence and are then often linked to customer databases to derive a measure of the level of consumption expected from the different area typologies. However, these systems ignore fundamental relationships in the retail market by focusing upon demand characteristics in a ‘vacuum’ and ignore the supply side and consumer-supplier interaction. This paper argues that there may be considerable advantages to including supply-side indicators within geodemographic systems. Whilst the term ‘supply’ in this context might imply the number of consumer services already in an area, equally important for understanding demand are variables such as the supply of jobs and houses. We suggest that profiling an area in terms of its labour market characteristics gives a better insight into the income chain while the supply of houses could be argued to be a crucial factor in household formation that in turn will impact upon demographic structure. Using the regional example of Yorkshire and Humberside in northern England, we indicate how a suite of supply-side variables relating to the labour market can be assembled and used alongside a suite of demand variables to generate a new area classification. Spatial interaction models are calibrated to derive some of the variables that take into account zonal self-containment and catchment size

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions