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

E-Governance, Metropolitan Governance and Development Programming. The Case of the Thessaloniki Metropolitan Area

e-Governance has recently emerged as a new field of interest for both researchers and public policy makers. This has to do in the first instance with the rise of information and communication technologies and with the strategy for promotion of the information society. It also reflects growing interest in the capacity of various forms of governance to manage complex development issues and facilitate decision-making in the era of globalization. The potential of e-Governance extends from improvement of public services at the various levels of administration to empowerment of community engagement within decision-making processes. e-Governance is also of manifest relevance to questions such as the digital divide and democratic participation. Metropolitan areas in particular are considered to be at the centre of the developmental process. They thus become the appropriate spatial level for the implementation of development programmes aimed at enhancement of competitiveness and employment. New forms of multilevel metropolitan governance emerge, in response to the economic and institutional transformations occurring in them. e-Governance represents a new challenge for metropolitan governance and in particular for development programming. In the context of the EU structural regional policy, development programming in Greece identifies the development of metropolitan areas as one of its main policy objectives. e-Governance is in any case a basic component of the Information Society strategy. This paper examines the implementation of e-Governance in the Thessaloniki metropolitan area, in the specific context of development programming. From this starting point, lessons are drawn for the necessity of e-Governance as an element of metropolitan governance.

The Repast Simulation/Modelling System for Geospatial Simulation

The use of simulation/modelling systems can simplify the implementation of agent-based models. Repast is one of the few simulation/modelling software systems that supports the integration of geospatial data especially that of vector-based geometries. This paper provides details about Repast specifically an overview, including its different development languages available to develop agent-based models. Before describing Repast’s core functionality and how models can be developed within it, specific emphasis will be placed on its ability to represent dynamics and incorporate geographical information. Once these elements of the system have been covered, a diverse list of Agent-Based Modelling (ABM) applications using Repast will be presented with particular emphasis on spatial applications utilizing Repast, in particular, those that utilize geospatial data

Rural Areas in Lithuania: Significance, Development and Aid

Paper aims analysing the situation of rural areas of Lithuania as well as support got from the budget of European Union facilitating the development of rural areas. Paper comprises three chapters. The rural areas are of substantial importance in terms of both surface area and population. At the beginning of 2002, the rural areas covered 63.6 thousand sq. km. This equalled 97.4 % of the total Lithuanian surface area. In 2003, the rural population was estimated at 1,145 million inhabitants. At the end of 2001 the total rural working population made up to 30 per cent of total Lithuanian employment, while agriculture, forestry and fishery employment rate was estimated at 17,8 per cent. Since Lithuania has joined the European Union in May 2004, population of rural areas starting to receive significant financial aid from the European budget. Largest share of EU financiers is going to be redistributed as the direct payments constituting in 2004 55 per cent of EU level. Programmes and finances, targeted directly to support the rural development, are considered in the so-called programming documents, prepared by the Government and approved by the European Commission.rural areas, rural development, EU financial aid, Lithuania, Community/Rural/Urban Development,

Integrated urban evolutionary modeling

Cellular automata models have proved rather popular as frameworks for simulating the physical growth of cities. Yet their brief history has been marked by a lack of application to real policy contexts, notwithstanding their obvious relevance to topical problems such as urban sprawl. Traditional urban models which emphasize transportation and demography continue to prevail despite their limitations in simulating realistic urban dynamics. To make progress, it is necessary to link CA models to these more traditional forms, focusing on the explicit simulation of the socio-economic attributes of land use activities as well as spatial interaction. There are several ways of tackling this but all are based on integration using various forms of strong and loose coupling which enable generically different models to be connected. Such integration covers many different features of urban simulation from data and software integration to internet operation, from interposing demand with the supply of urban land to enabling growth, location, and distributive mechanisms within such models to be reconciled. Here we will focus on developin

Using Machine Learning for Handover Optimization in Vehicular Fog Computing

Smart mobility management would be an important prerequisite for future fog computing systems. In this research, we propose a learning-based handover optimization for the Internet of Vehicles that would assist the smooth transition of device connections and offloaded tasks between fog nodes. To accomplish this, we make use of machine learning algorithms to learn from vehicle interactions with fog nodes. Our approach uses a three-layer feed-forward neural network to predict the correct fog node at a given location and time with 99.2 % accuracy on a test set. We also implement a dual stacked recurrent neural network (RNN) with long short-term memory (LSTM) cells capable of learning the latency, or cost, associated with these service requests. We create a simulation in JAMScript using a dataset of real-world vehicle movements to create a dataset to train these networks. We further propose the use of this predictive system in a smarter request routing mechanism to minimize the service interruption during handovers between fog nodes and to anticipate areas of low coverage through a series of experiments and test the models' performance on a test set