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Urban Park green space is an essential carrier and form of urban recreation space, playing an important role in improving the ecological environment and enhancing the image of the city. With the process of urbanization and the continuous improvement of residents’ living standards, urban park construction has become increasingly important to meet people’s growing needs for a better life. However, the evaluation indicators for urban park green space in China mostly consider macroscopic levels, such as the area, number and per capita green area of parks. This cannot fully reflect the level of park green space services. Accessibility can reflect the convenience of residents to reach the park, provide guidance for the reasonable layout of parks and have various evaluation methods relying on geographic information technology. Therefore, it is necessary to quantitatively study the layout and accessibility of park green space. Taking the central urban area of Nanjing as the research object, this paper summarizes existing research and focuses on evaluating the accessibility of park green space while analyzing the layout of parks from multiple perspectives, combined with the existing pressure appraisal of park green space services. Relevant data on Nanjing, including overall planning and statistical yearbooks, were collected and analyzed using geographic information system (GIS) tools, network analysis and Thiessen polygon theory to analyze the service pressure, demand and accessibility of park green space in the central urban area under different transportation methods. Based on the results, targeted optimization strategies are proposed.

Under the background of accelerating urbanization and increasing stress of ecological environment, the construction of livable city has attracted extensive attention and become a hot spot in the study of urban problems in the world. The evaluation of livable city is a reference for the comparison of urban development and also one of the evaluation criteria for the comparison of urban competitiveness. This paper focuses on three different evaluation factors of ecological environment, economic development and public service to construct an evaluation model of environmental quality of livable cities. Then particle swarm optimization (PSO) is introduced to optimize the parameters of support vector machine (SVM), and a SVM algorithm based on PSO (PSO-SVM) is proposed to solve the livable city evaluation model. Finally, the spatial analysis combined with ArcGIS software obtained the livable city evaluation and division results of Hunan Province. The results show that PSO-SVM algorithm is superior to SVM, BA-SVM, GA-SVM, and has the advantages of faster speed and higher classification accuracy.

On-site wastewater treatment facilities (WWTFs) collect, treat, and dispose wastewater from dwellings that are not connected to municipal wastewater collection and treatment systems. They serve about 25% of the total population in the United States from an estimated 26 million homes, businesses, and recreational facilities nationwide. There is currently no adequate coordinated information management system for on-site WWTFs. Given the increasing concern about environmental contamination and its effect on public health, it is necessary to provide a more adequate management tool for on-site WWTFs information. This paper presents the development of an integrated, GIS-based, on-site wastewater information management system, which includes three components: (1) a mobile GIS for field data collection; (2) a World Wide Web (WWW) interface for electronic submission of individual WWTF information to a centralized GIS database in a state department of public health or state environmental protection agency; and (3) a GIS for the display and management of on-site WWTFs information, along with other spatial information such as land use, soil types, streams, and topography. It is anticipated that this GIS-based on-site wastewater information management system will provide environmental protection agencies and public health organizations with a spatial framework for managing on-site WWTFs and assessing the risks related to surface discharges.

The state of the art in location models falls short to adapt to new requirements such as composition and integration of maps. Composing and integrating maps are typical operations we want to apply when we deal with ubiquitous computing applications because they evolve permanently (i.e. to add location information of new cities, buildings, means of transport, etc.). We propose a novel approach that by abstracting some concepts such as located objects and locations, can result in more flexible models, therefore allowing dynamic composition and integration of maps. With this approach, it is also possible to combine different location representations, making applications easier to extend.

A rectilinear map consists of a set of mutually non-intersecting rectilinear (i.e., horizontal or vertical) line segments, and each segment is allowed to use a rectangular label of height B and length the same as the segment. Sliding labels are not restricted to any finite number of predefined positions but can slide and be placed at any position as long as it intersects the segment. This paper considers three versions of the problem of labeling a rectilinear map with sliding labels and presents efficient exact and approximation algorithms for them.

Driven by the industrial challenge of labeling maps for GIS applications, we investigate the problem of computing a map region P such that a rectangular axis-parallel label L of a given size can be placed in it. The map region to be labeled is in general a non-convex n-gon which may contain holes. We first derive a new practical algorithm based on the sweep-line technique that determines the com set of Maximum Inscribed Rectangles (MIRs) in P in O(nk), where k is the size of the output, for the case when the polygon sides have an axis-parallel orientation. After the set of MIRs has been found, any subsequent query on label L placement runs in only O(logn) time. We then provide an algorithm to convert the general case to the axis-parallel case. Extensive experimentation in both laboratory and industrial settings confirms that the developed method is practical and highly efficient for processing large GIS data sets.

Among real-system applications of AI, the field of traffic simulation makes use of a wide range of techniques and algorithms. Especially, microscopic models of road traffic have been expanding for several years. Indeed, Multi-Agent Systems provide the capability of modeling the very diversity of individual behaviors. Several professional tools provide comprehensive sets of ready-made, accurate behaviors for several kinds of vehicles. The price in such tools is the difficulty to modify the nature of programmed behaviors, and the specialization in a single purpose, e.g. either studying resulting ows, or providing an immersive virtual reality environment. Thus, we advocate for a more exible approach for the design of multi-purpose tools for decision support. Especially, the use of geographical open databases offers the opportunity to design agent-based traffic simulators which can be continuously informed of changes in traffic conditions. Our proposal also makes decision support systems able to integrate environmental and behavioral modifications in a linear fashion, and to compare various scenarios built from different hypotheses in terms of actors, behaviors, environment and ows. We also describe here the prototype tool that has been implemented according to our design principles.

This paper introduces a new technique in ecology to analyze spatial and temporal variability in environmental variables. By using simple statistics, we explore the relations between abiotic and biotic variables that influence animal distributions. However, spatial and temporal variability in rainfall, a key variable in ecological studies, can cause difficulties to any basic model including time evolution.

The study was of a landscape scale (three million square kilometers in eastern Australia), mainly over the period of 1998–2004. We simultaneously considered qualitative spatial (soil and habitat types) and quantitative temporal (rainfall) variables in a Geographical Information System environment. In addition to some techniques commonly used in ecology, we applied a new method, Functional Principal Component Analysis, which proved to be very suitable for this case, as it explained more than 97% of the total variance of the rainfall data, providing us with substitute variables that are easier to manage and are even able to explain rainfall patterns. The main variable came from a habitat classification that showed strong correlations with rainfall values and soil types.

Nowadays, translating information about hydrologic and soil properties and processes across scales has emerged as a major theme in soil science and hydrology, and suitable theories for upscaling or downscaling hydrologic and soil information are being looked forward. The recognition of low-order catchments as self-organized systems suggests the existence of a great amount of links at different scales between their elements. The objective of this work was to research in areas of homogeneous bedrock material, the relationship between the hierarchical structure of the drainage networks at hillslope scale and the heterogeneity of the particle-size distribution at pedon scale. One of the most innovative elements in this work is the choice of the parameters to quantify the organization level of the studied features. The fractal dimension has been selected to measure the hierarchical structure of the drainage networks, while the Balanced Entropy Index (BEI) has been the chosen parameter to quantify the heterogeneity of the particle-size distribution from textural data. These parameters have made it possible to establish quantifiable relationships between two features attached to different steps in the scale range. Results suggest that the bedrock lithology of the landscape constrains the architecture of the drainage networks developed on it and the particle soil distribution resulting in the fragmentation processes.

In this paper, a GIS-based method was developed to extract the real-time traffic information (RTTI) from the Google Maps system for city roads. The method can be used to quantify both congested and free-flow traffic conditions. The roadway length was defined as congested length (CL) and free-flow length (FFL). Chengdu, the capital of Sichuan Province in the southwest of China, was chosen as a case study site. The RTTI data were extracted from the Google real-time maps in May 12–17, 2013 and were used to derive the CL and FFL for the study areas. The Multifractal Detrended Fluctuation Analysis (MFDFA) was used to characterize the long-term correlations of CL and FFL time series and their corresponding multifractal properties. Analysis showed that CL and FFL had demonstrated time nonlinearity and long-term correlations and both characteristics differed significantly. A shuffling procedure and a phase randomization procedure were further integrated with multifractal detrending moving average (MFDMA) to identify the major sources of multifractality of these two time series. The results showed that a multifractal process analysis could be used to characterize complex traffic data. Traffic data collected and methods developed in this paper will help better understand the complex traffic systems.

A digital city is a social information infrastructure for urban life (including shopping, business, transportation, education, welfare and so on). We started a project to develop a digital city for Kyoto based on the newest technologies including cooperative information agents. This paper presents an architecture for digital cities and shows the roles of agent interfaces in it. We propose two types of cooperative information agents as follows: (a) the front-end agents determine and refine users' uncertain goals, (b) the back-end agents extract and organize relevant information from the Internet, (c) Both types of agents opportunistically cooperate through a blackboard. We also show the research guidelines towards social agents in digital cities; the agent will foster social interaction among people who are living in/visiting the city.

Genetic Codes.

Detection Kits.

WHO Network.

SARS Virus Mutating Into At Least Two Forms.

The Resilient SARS Bug.

Singapore Team Announces New Findings on SARS Virus.

Amplifying Spatial Awareness via GIS — Tech which brings Healthcare Management, Preventative & Predictive Measures under the same Cloud

When it is not just about size, you gotta' be Smart, too!

Chew on It! How Singapore-based health informatics company MHC Asia Group crunches big-data to uncover your company's health

Digital tool when well-used, it is Passion

Carving the Digital Route to Wellness

Big Data, Bigger Disease Management and Current preparations to manage the Future Health of Singaporeans

A Conversation with Mr Arun Puri

Extreme Networks: Health Solutions

Big Data in Clinical Research Sector

This paper proposes a semi-automatic method of geographic information linking based on spatial relationships, entity names, entity categories and other features, combined with semantic and machine learning methods. First, we extracted geographic information from three geographic information sources: Open Street Map, Wikimapia, and Google places. The extracted geographic information is mainly for urban buildings in different regions. Secondly, we analyzed and extracted the characteristics of geographic information data to construct a geographic information ontology, and realized the integration of geographic data through the mapping of geographic information source data and geographic information ontology. Finally, the linking method of fusion classification algorithm support vector machine and K-nearest neighbor method are discussed separately. At the same time, it is compared with the linking method proposed by Samal et al. to comprehensively verify the accuracy of the method proposed in this paper from multiple angles, laying a good foundation for seismic information integration.

Geographical information system (GIS)-based noise simulation software (N-GNOIS) has been developed to simulate the noise scenario due to point and mobile sources considering the impact of geographical features and meteorological parameters. These have been addressed in the software through attenuation modules of atmosphere, vegetation and barrier. N-GNOIS is a user friendly, platform-independent and open geospatial consortia (OGC) compliant software. It has been developed using open source technology (QGIS) and open source language (Python). N-GNOIS has unique features like cumulative impact of point and mobile sources, building structure and honking due to traffic. Honking is the most common phenomenon in developing countries and is frequently observed on any type of roads. N-GNOIS also helps in designing physical barrier and vegetation cover to check the propagation of noise and acts as a decision making tool for planning and management of noise component in environmental impact assessment (EIA) studies.

This article focuses on the integration of multicriteria decision analysis (MCDA) and geographical information systems (GIS) and introduces a tool, GIS–MCDA, written in visual basic in ArcGIS for GIS-based MCDA. The GIS–MCDA deals with raster-based data sets and includes standardization, weighting and decision analysis methods, and sensitivity analysis. Simple additive weighting, weighted product method, technique for order preference by similarity to ideal solution, compromise programming, analytic hierarchy process, and ordered weighted average for decision analysis; ranking, rating, and pairwise comparison for weighting and linear scale transformation for standardization can be applied by using this tool. The maximum score and score range procedures can be used for linear scale transformation. In this article also an application of the GIS–MCDA to determine the flood vulnerability of the South Marmara Basin in Turkey is examined. To check the validity and reliability of the results, the flood vulnerability layer is compared with flood-affected areas.

The ability or inability to develop an effective, reliable supplier network can often play a major role in determining an organization’s competitive position. Especially in today’s era of a complex global economy, disruptions to an organization’s supply chain can drastically undermine its ability to compete. We analyze the interaction between density risk, or risk related to the proximal relationships between suppliers, and environmental risk, or risk arising from conditions affecting a supplier’s local business environment. We provide a powerful supply base risk mitigation strategy incorporating spatial analytics to enhance our analyses. We develop a multi-objective program to manage these factors and recommend minimal risk supply bases. We detail the interaction between objectives in an example and discuss the ramifications for managers. This work will assist managers in their efforts to build a supply base that meets the cost and efficiency demands of their organization.

Rapid motorization and uncertainty in urban growth patterns make parking space management a serious task, especially in middle-income developing countries, and this has severe social, economic, and environmental repercussions including increased congestion, crash frequency, fuel and time consumption, and air pollution. Due to the complexity of the urban transportation issue and the wide variety of variables involved, a multicriteria assessment is essential. This study used fuzzy logic and geographical information systems (GIS) to develop a multi-criteria decision making (MCDM) model for managing parking in Shiraz’s central business district (CBD). The literature was mined for information on the variables that affect parking site placement, and a poll of experts ($n=11$) was used to determine their relative importance. The distance to travel attraction centers, distance to roads, land price, population density, and available land for multi-storey parking were among the factors considered. Meanwhile, the parking space shortage for each TAZ is calculated by subtracting the estimated parking space supply from the estimated parking space demand. An overlay of these two layers distinguishes locations that are in parking shortage zones and also meet multiple criteria. The results may aid policymakers in controlling parking demand by pinpointing the most promising places for investment.

Information systems (IS) and data analytics-focused academic disciplines remained surprisingly silent in attempting to contribute to a public understanding of critical societal challenges such as foreclosures. This paper tackles the gap by presenting a framework for building foreclosure prediction models by integrating publicly-available census-tract demographic data and readily-available technology (geographic IS (GIS) and machine learning (ML)). The framework is tested and validated using over 19,000 foreclosures from Cuyahoga County (OH) using J48 decision tree, artificial neural network, and Naive Bayes algorithms. The framework’s empirical test identifies nine critical demographic attributes to successfully predict foreclosures, confirming the findings of prior studies while offering several new, highly predictive variables that were missed by prior research. This research is a call to broader IS, CS, and data science communities to assist society in understanding critical societal issues that may need deploying and integrating more advanced technologies.