The stock-flow model of spatial data infrastructure development refined by fuzzy logic

The system dynamics technique has been demonstrated to be a proper method by which to model and simulate the development of spatial data infrastructures (SDI). An SDI is a collaborative effort to manage and share spatial data at different political and administrative levels. It is comprised of various dynamically interacting quantitative and qualitative (linguistic) variables. To incorporate linguistic variables and their joint effects in an SDI-development model more effectively, we suggest employing fuzzy logic. Not all fuzzy models are able to model the dynamic behavior of SDIs properly. Therefore, this paper aims to investigate different fuzzy models and their suitability for modeling SDIs. To that end, two inference and two defuzzification methods were used for the fuzzification of the joint effect of two variables in an existing SDI model. The results show that the Average–Average inference and Center of Area defuzzification can better model the dynamics of SDI development

Modeling and improving Spatial Data Infrastructure (SDI)

Spatial Data Infrastructure (SDI) development is widely known to be a challenging process owing to its complex and dynamic nature. Although great effort has been made to conceptually explain the complexity and dynamics of SDIs, few studies thus far have actually modeled these complexities. In fact, better modeling of SDI complexities will lead to more reliable plans for its development. A state-of-the-art simulation model of SDI development, hereafter referred to as SMSDI, was created by using the system dynamics (SD) technique. The SMSDI enables policy-makers to test various investment scenarios in different aspects of SDI and helps them to determine the optimum policy for further development of an SDI. This thesis begins with adaption of the SMSDI to a new case study in Tanzania by using the community of participant concept, and further development of the model is performed by using fuzzy logic. It is argued that the techniques and models proposed in this part of the study enable SDI planning to be conducted in a more reliable manner, which facilitates receiving the support of stakeholders for the development of SDI.Developing a collaborative platform such as SDI would highlight the differences among stakeholders including the heterogeneous data they produce and share. This makes the reuse of spatial data difficult mainly because the shared data need to be integrated with other datasets and used in applications that differ from those originally produced for. The integration of authoritative data and Volunteered Geographic Information (VGI), which has a lower level structure and production standards, is a new, challenging area. The second part of this study focuses on proposing techniques to improve the matching and integration of spatial datasets. It is shown that the proposed solutions, which are based on pattern recognition and ontology, can considerably improve the integration of spatial data in SDIs and enable the reuse or multipurpose usage of available data resources

SDI strategic planning using the system dynamics technique: A case study in Tanzania

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.Development of spatial data Infrastructure (SDI) is a long term process, which requires long-term plans. The complexity of SDI, which is a matter of technical, institutional and financial challenges and their interactions, makes the development of such a plan complicated. It is also generally hard to convince policy-makers about the reliability of a plan and the future effect of that to get their supports. The system dynamics technique has been shown to be a proper approach for SDI planning, responding to the above issues. This paper summarizes the application of the system dynamics technique for SDI modelling in Tanzania

Earthquake Early Warning and Beyond: Systems Challenges in Smartphone-based Seismic Network

Earthquake Early Warning (EEW) systems can effectively reduce fatalities, injuries, and damages caused by earthquakes. Current EEW systems are mostly based on traditional seismic and geodetic networks, and exist only in a few countries due to the high cost of installing and maintaining such systems. The MyShake system takes a different approach and turns people's smartphones into portable seismic sensors to detect earthquake-like motions. However, to issue EEW messages with high accuracy and low latency in the real world, we need to address a number of challenges related to mobile computing. In this paper, we first summarize our experience building and deploying the MyShake system, then focus on two key challenges for smartphone-based EEW (sensing heterogeneity and user/system dynamics) and some preliminary exploration. We also discuss other challenges and new research directions associated with smartphone-based seismic network.Comment: 6 pages, conference paper, already accepted at hotmobile 201

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Modeling office firm dynamics in an agent-based micro simulation framework : methods and empirical analysis

Office firms represent a large share of economic activities, especially in the sector of professional services. In general, firms will follow an evolutionary cycle comprising the dynamics of starting-up, finding a location to establish their business, growing or declining, relocating and going out of business. The underlying approach taken in this research project relies on the idea that the evolution of office firms is strongly influenced by the urban environment. Traditionally, the specific relationship between transportation and land use has been examined in the framework of aggregate integrated land use-transportation (LUTI) models. However, the field is moving toward a more disaggregate approach, based on concepts of micro simulation and agent-based models. These are built on behaviorally richer concepts for examining firm dynamics, such as firm demography. The aim of this research project is to contribute to this emerging field by developing an agent-based modeling approach to simulate the evolution of office firms in time and space. To this end, a set of statistical/econometric models is used to investigate the relationships between specific firm demographic processes and the urban environment. The research project contributes to the existing literature by focusing on office firm demography and related land use and transportation influences, exploring alternative approaches to model office firm dynamics empirically, and using very detailed nationwide data from The Netherlands

Mediterranean Desertification and the Economic System

This paper reviews the role of market, population growth, social issues, developmental policies, and other (minor) economic variables contributing to Mediterranean desertification. These variables were classified as describing the micro-economic and macro-economic factors suitable to assure a better comprehension of the environmental-economic nexus. Micro-economic factors like the higher prices and lower wages in the primary sector, as well as the reduction of off-farm employment reflect some potential causes of LD. It was also argued how technical change, agricultural input prices, and household income may affect land vulnerability but their contribution to this ecological problem is poorly known. On the contrary, the role of macroeconomic factors such as population density, poverty, and environmental policies, although more extensively studied on a qualitative base, was regarded as important but still relatively ambiguous, and needs further quantitative studies. Territorial disparities in land distribution, as well as increasing rural poverty and unsustainable management of soil and water were described as a consequence of the process triggering Mediterranean desertification. The effectiveness of policies aimed at mitigating LD and thus reducing desertification risk was finally discussed.Land degradation, desertification, economic system, micro-economic causes, macro-economic factors, Mediterranean basin

Spatiotemporal patterns and predictability of cyberattacks

A relatively unexplored issue in cybersecurity science and engineering is whether there exist intrinsic patterns of cyberattacks. Conventional wisdom favors absence of such patterns due to the overwhelming complexity of the modern cyberspace. Surprisingly, through a detailed analysis of an extensive data set that records the time-dependent frequencies of attacks over a relatively wide range of consecutive IP addresses, we successfully uncover intrinsic spatiotemporal patterns underlying cyberattacks, where the term "spatio" refers to the IP address space. In particular, we focus on analyzing {\em macroscopic} properties of the attack traffic flows and identify two main patterns with distinct spatiotemporal characteristics: deterministic and stochastic. Strikingly, there are very few sets of major attackers committing almost all the attacks, since their attack "fingerprints" and target selection scheme can be unequivocally identified according to the very limited number of unique spatiotemporal characteristics, each of which only exists on a consecutive IP region and differs significantly from the others. We utilize a number of quantitative measures, including the flux-fluctuation law, the Markov state transition probability matrix, and predictability measures, to characterize the attack patterns in a comprehensive manner. A general finding is that the attack patterns possess high degrees of predictability, potentially paving the way to anticipating and, consequently, mitigating or even preventing large-scale cyberattacks using macroscopic approaches