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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.

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 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.

This chapter is devoted to aerospace images and raster topographic maps processing and recognition methods application to create and update vector maps and digital elevation models (DEMs), that form geoinformation basis to solve computational problems (for instance, relocation routes multicriterial construction) and visualize terrain.

Methods for objects with different geometric shapes detection in set up by user calibration points on aerospace images are considered. The method provides automatic detection of the object’s parameters (for instance, rectangle’s position, orientation, and aspect ratio). A method for buildings’ and constructions’ projection to the basement, providing their true geographic positions definition, and a method for objects’ shades detection, designed to calculate their heights over the underlying surface, are described. Experimental investigations of software instruments, created using mentioned above methods, are shown. Vectorization precision and velocity values compared to known analogs are presented.

Methods for isolines detection, height marks, and isolines’ signatures recognition on raster topographic maps, that were scanned from paper medium, and their composition to vector and digital elevation models are described. Experimental investigation results, namely, precision and velocity values, of DEM construction in automatic and automated modes are presented.

Forests are essential for survival and sustenance of life. Their growth should be optimized so that greater benefits are derived from them. With such a large establishment and geographical base, the monitoring and decision making becomes very critical. The inherent delays hamper the decision process required at a particular time. The increasing area covered by forest plantations creates a demand for trustworthy mechanisms to ensure they are responsibly established and managed. However, most are focused exclusively or prevalently on natural or semi-natural forests, while only a few are specific to planted forests or plantations. The main aim is to assess whether and to what extent planted forests are properly considered within the existing sets of standards/guidelines and to identify areas for improvements, is based on a series of comparative analysis. This paper focus to carry out the full potential of convergence of GIS and Mobile Technology for plantation, with emphasis on technically viable infrastructure solution based on sustainability principles. Integration of GIS and Mobile is being proposed with an objective to enable a single window access to information and services being provided by various formations and to establish a collaborated environment.