Potential of geographic information systems in building cost estimation and visualization

The management system which is based on handling GIS database files and remotely sensed imageries on Geographical Information System (GIS) environment is an essential tool to guide and regulate the production systems and marketing activities of product chains. In the case of guava, the management system has to be fed with a specific and structured field database and its metadata to verify the phenotypic status of guava trees and to characterize the production system applied by the producer in agronomic terms (coordinate data, planting frame, vigor, health etc). This data is useful to determine agronomic conditions to be incorporated as a field test in a supervised classification process on SPOT satellite images (PAN and MS) which is used as a mean to scale the punctual field observation data. As a numerical value, the raster structure of satellite images and the numeric value for each cell represents guava photosynthetic activity rate. Along with this, the isolation of guava's phenotypic signature in DN units will feed the supervised classification procedure allowing the user to generate thematic maps per class. On GIS environment, a multi-assessment analysis is applied in order to assign a weight to each component on the project. The analysis is based on spatial analysis vicinity functions applied to guava orchard locations, proximity to collecting centers, roads, and highways. The visual immediacy on thematic maps will allow the decision makers to implement their own criteria analysis to plan the market destination of guava production volume weeks after harvest (fresh market or industry) in Calvillo County.

This review article aims to discuss the current status and future potential of Geographic Information Systems (GIS) in map-based technology of tsunami risk management, especially in seismically active, well-known tsunami regions of the world. It presents GIS technologies for hazard mapping, risk assessment, and information generation for disaster-response operations. These are important tools for accurately mapping vulnerable areas by integrating real-time and historical data to develop accurate forecasts for possible tsunamis. Demographic and geographic data were also analyzed by GIS to determine the optimum route to develop evacuation strategies. A set of case studies demonstrates how GIS improves community resilience by supporting informed decision-making. In addition, suggestions are made for how future steeps as the integration of machine learning techniques as emerging tools for analyzing and classifying complex and vast datasets, which may enhance GIS applications in tsunami risk management to improve the accuracy and utility of these tools.

This study proposes a probabilistic analysis method for modeling rainfall-induced shallow landslide susceptibility by combining a transient infiltration flow model and Monte Carlo simulations. The spatiotemporal change in pore water pressure over time caused by rainfall infiltration is one of the most important factors causing landslides. Therefore, the transient infiltration hydrogeological model was adopted to estimate the pore water pressure within the hill slope and to analyze landslide susceptibility. In addition, because of the inherent uncertainty and variability caused by complex geological conditions and the limited number of available soil samples over a large area, this study utilized probabilistic analysis based on Monte Carlo simulations to account for the variability in the input parameters. The analysis was performed in a geographic information system (GIS) environment because GIS can deal efficiently with a large volume of spatial data. To evaluate its effectiveness, the proposed analysis method was applied to a study area that had experienced a large number of landslides in July 2006. For the susceptibility analysis, a spatial database of input parameters and a landslide inventory map were constructed in a GIS environment. The results of the landslide susceptibility assessment were compared with the landslide inventory, and the proposed approach demonstrated good predictive performance. In addition, the probabilistic method exhibited better performance than the deterministic alternative. Thus, analysis methods that account for uncertainties in input parameters are more appropriate for analysis of an extensive area, for which uncertainties may significantly affect the predictions because of the large area and limited data.

In the nuclear power industry, Level 3 probabilistic risk assessment (PRA) is used to estimate damage to public health and the environment if a severe accident leads to large radiological release. Current Level 3 PRA does not have an explicit inclusion of social factors and, therefore, it is not possible to perform importance ranking of social factors for risk-informing emergency preparedness, planning, and response (EPPR). This article offers a methodology for adapting the concept of social vulnerability, commonly used in natural hazard research, in the context of a severe nuclear power plant accident. The methodology has four steps: (1) calculating a hazard-independent social vulnerability index for the local population; (2) developing a location-specific representation of the maximum radiological hazard estimated from current Level 3 PRA, in a geographic information system (GIS) environment; (3) developing a GIS-based socio-technical risk map by combining the social vulnerability index and the location-specific radiological hazard; and (4) conducting a risk importance measure analysis to rank the criticality of social factors based on their contribution to the socio-technical risk. The methodology is applied using results from the 2012 Surry Power Station state-of-the-art reactor consequence analysis. A radiological hazard model is generated from MELCOR accident consequence code system, translated into a GIS environment, and combined with the Center for Disease Control social vulnerability index (SVI). This research creates an opportunity to explicitly consider and rank the criticality of location-specific SVI themes based on their influence on risk, providing input for EPPR.

<p>Landslides are among the top five natural disasters in terms of casualties and property damage; therefore, landslide susceptibility mapping is vital in landslide-prone areas, particularly hilly terrain. Globally, landslides alone take away the life of 17% of the total death caused by natural hazards. The death count in numbers is approximately 1000 per year, with property damage of about US$ 4 billion. This makes the study of landslides extremely significant. Important factors contributing to the reported global increase in landslides are the rapid growth of the world's population, urbanisation in the developing world, and global climate change.</p><p>Landslides are natural and anthropogenic hazards that have impacted Indian subcontinent, especially the Himalayas and other mountainous areas. Comparative evaluations of the landslide susceptibility mapping models are necessary for landslide susceptibility mapping to find the best fit model for the specific area. The present study has been conducted in the West Sikkim district of India, in the Indian Himalayan Region, using a data-driven statistical model of information value method (IVM) and frequency ratio method (FRM), as well as a knowledge-driven heuristic approach of analytic hierarchy process (AHP). The combination of the statistical and the knowledge-based approach is applied because the former gives the unbiased result based on the pixel value of the satellite data used, whereas the knowledge-based method gives the value based on the knowledge and experience of the expert, so a very good comparison can be made. In this study, eleven landslide conditioning factors were analysed in the remote sensing (RS) and geographic information system (GIS) environment, which are slope aspect, slope gradient, slope curvature, drainage density, elevation, lithology, land use and land cover (LULC), normalised difference vegetation index (NDVI), geomorphology, lineament density, and soil type. The Resourcesat 2A satellite images were used from Indian remote sensing agency having LISS 4 sensors of 5.8 m resolution data.</p><p>A total of 685 landslides were identified in a satellite image, and the polygons of the same in the shapefile format mapped in the GIS environment. Landslides mapped from the satellite data has also been validated in the google earth images and selected sites are also validated by ground truthing. 70% of the total landslide polygons were taken as the training data the remaining 30% landslide polygons were taken for the validation purpose The studies were validated using a receiver operating characteristic curve that fit the model with acceptable values of more than 60% for all three models, with the highest value of 74% being obtained for the information value method. The density distribution method has validated the result, confirmed by the landslide density increased from the low susceptibility zone to the high susceptibility zone. These types of studies are helpful for the decision-makers and the planners for the developmental projects that are ongoing in the state and future projects.</p>

In this study, index of entropy and catastrophe theory methods were used for demarcating groundwater potential in an arid region using weighted linear combination techniques in geographical information system (GIS) environment. A case study from Badra area in the eastern part of central of Iraq was analyzed and discussed. Six factors believed to have influence on groundwater occurrence namely elevation, slope, aquifer transmissivity and storativity, soil, and distance to fault were prepared as raster thematic layers to facility integration into GIS environment. The factors were chosen based on the availability of data and local conditions of the study area. Both techniques were used for computing weights and assigning ranks vital for applying weighted linear combination approach. The results of application of both modes indicated that the most influential groundwater occurrence factors were slope and elevation. The other factors have relatively smaller values of weights implying that these factors have a minor role in groundwater occurrence conditions. The groundwater potential index (GPI) values for both models were classified using natural break classification scheme into five categories: very low, low, moderate, high, and very high. For validation of generated GPI, the relative operating characteristic (ROC) curves were used. According to the obtained area under the curve, the catastrophe model with 78 % prediction accuracy was found to perform better than entropy model with 77 % prediction accuracy. The overall results indicated that both models have good capability for predicting groundwater potential zones.

In this research a topography indicator map was produced to show slope and landforms. The process involved the use of appropriate software in the Geographic Information System (GIS) environment. Different data was collected basically from Shuttle Radar Topography Mission (SRTM) and Global Positioning System(GPS) of the study area, these was integrated in the GIS environment using appropriate software. The Digital Elevation Model(DEMs) from STRM was preprocessed, data analyzed and then colors were assigned to relevant features to make the map readable. These type of maps produced can be used in Environmental Management, Urban planning, Agriculture and Hydrology.

This chapter introduces geographical information systems (GIS) by describing some fundamental concepts important to them in more detail. It aims to help the newcomer to GIS and to provide some understanding of what GIS are and what they can be used for. The concept of GIS will be described in a number of ways: formal definition, examination of distinguishing features in regard to other information systems, the type of questions it can help answer, its ability to carry out certain specific operations and, listing the components a GIS comprises. In addition, the subject matters of this chapter are both the latest developments in the fields of hardware and software in a GIS environment and future directions and trends in these fields. As the concept of GIS not only includes hardware and software, this chapter will, in addition, briefly consider types of data and data storage and the different types of user groups and their differentiated GIS needs.

The frequency and intensity of flood disasters have become serious issues in the national development process of Nigeria as flood disasters have caused serious environmental damages, loss of human lives and other heavy economic losses; putting the issue of disaster reduction and risk management higher on the policy agenda of affected governments, multilateral agencies and NGOs. The starting point of concrete flood disaster mitigation efforts is to identify the areas with higher risk levels and fashion out appropriate preventive and response mechanisms. This research paper explored the potentials of Geographic Information System (GIS) in data capture, processing and analysis in identifying flood-prone areas for the purpose of planning for disaster mitigation and preparedness, using Benin-Owena river basin of Nigeria as a unit of analysis. The data used in this study were obtained from FORMECU and were entered and use to develop a flood risk information system. Analysis and capability of the developed system was illustrated and shown graphically. The research showed that over one thousand settlements harbouring over ten million people located in the study area are at grave risk of flooding. Key words: Flood, Risk, Vulnerability, Geographical Information System (GIS), River -Basin

In basin researches, quantitative data available from Topographical maps and Digital Elevation Model (DEM) make significant contributions to researchers in line with database created making use of Geographical Information Systems (GIS). Technological developments in the last 10 years, in particular, rendered GIS and Remote Sensing methods fit-for-use to achieve the objectives in geology-related applications. To identify applications of GIS in relation to basin researches, we selected the basin of the Kazandere Stream Basin of the castle located in Marmara Region as the field of study. In this study, we generated drainage network features of the basin using Arcinfo 9.2 GIS software from streams on 1/25000 scaled topographical map and from 10 m grid spaced DEM. We used Archydro Tool in line with the objectives of study. We obtained parameters in relation to stream directory analyses such as branching rate, drainage density and stream frequency of the field of study making use of topographical maps. And for DEM-related applications, we obtained information about sub-basins by way of Flow Accumulation and Flow Direction model. DEM's are a general source of data for field analyses and 3-dimensional applications. Such basin characteristics as basin field, sub-basin fields, water running directions, drainage networks, catchment centreoid etc., are easily generated through DEM. Stream morphometric features found using such two data sources provide reliable information on drainage characteristics of the stream basin and its lower branches. Analyses and applications conducted within the framework of database created on GIS environment indicated that GIS could be used for multi-purposes under scope of geographical researches.

Fault system is a significant evidence of tectonic movement during crust tectonic evolution and may play an more important role in oil-gas accumulation process than other tectonic types in sedimentary basin. Carboniferous surface faults in Junggar Basin developed well and varied in size and distribution. There are about 200 faults in Carboniferous, and 187 of them are thrust faults. Chaos-fractals theories have been widely investigated and great progress has been made in the past three decades. One of the important conception-fractal dimension had become a powerful tool for describing non-linearity dynamical system characteristic. The clustered objects in nature are often fractal and fault system distribution in space is inhomogeneous, always occurs in groups, so we can describe spatial distribution of faults from the point of fractal dimension. Fractal dimension of fault system is a comprehensive factor associated with fault number, size, combination modes and dynamics mechanism, so it can evaluate the complexity of fault system quantitatively. The relationship between fault system and oil-gas accumulation is a focus and difficulty problem in petroleum geology, and fractal dimension is a new tool for describing fault distribution and predicting potential areas of hydrocarbon resources. Geographic Information System (GIS) is a kind of technological system collecting, storing, managing, computing, analyzing, displaying and describing the geospatial information supported by computer software and hardware. In the last 15-20 years, GIS have been increasingly used to address a wide variety of geoscience problems. Weights-of-evidence models use the theory of conditional probability to quantify spatial association between fractal dimension and oil-gas accumulation. The weights of evidence are combined with the prior probability of occurrence of oil-gas accumulation using Bayes'rule in a loglinear form under an assumption of conditional independence of the dimension maps to derive posterior probability of occurrence of oil-gas accumulation. In this paper, we first vectorize the fault system in Carboniferous of Junggar Basin in GIS software and store it as polyline layer in Geodatabase of GIS to manage and analyze, then calculate the fractal dimension of three types which are box dimension, information dimension and cumulative length dimension using spatial functions of GIS, in the last use weights-of-evidence model to calculate the correlation coefficients in GIS environment between oil-gas accumulation and three types of fractal dimension in order to quantity the importance of fault system.

Geographic Information Systems (GIS) are an important tool in the environmental management of protected areas, offering advanced technological methods for biodiversity protection and spatial planning.In the Divjakë-Karavasta National Park, the integration of GIS technology has significantly improved the monitoring and management of its rich ecosystems. The use of GIS in this park has enabled a detailed inventory of the health of vegetation, enabling the creation of accurate maps of the distribution of biodiversity.These maps are essential for identifying areas of special ecological importance and for determining appropriate conservation measures. GIS and RS technology provides a solid methodology used to evaluate and analyse different factors that influence a decision with regards to forest health. It helps assess risk to forest health by considering various ecological, climatic and environmental factors(temperatures, humidity, the spread of fires, air pollution etc.) affecting forest health, thus enabling improved management of the forest environment. A set of satellite images from SN32-Satellite Nusat 32 (Albania 1)/SN33-Satellite Nusat 33 (Albania 2), year 2024 are used as main data source to analyze risk factors in forest health of the park. Important indices namely Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Land Cover (LC) and Road infrastructure have been calculated in a GIS environment using ArcGIS Desktop 10.8.2/Pro 3.2. The spatial patterns that study reveal indicate that the majority of forest areas in Divjake-Karavasta National Park resides in an adequate environment under supportive health factors.Moreover, GIS enables real-time monitoring of environmental changes, such as the appearance of invasive species or habitat degradation, providing valuable data for quick and effective interventions. The integration of GIS in the management of the Divjakë-Karavasta. National Park has brought many benefits, but it has also encountered various challenges. One of the main challenges is the need for staff training in the use of advanced GIS technology and the provision of the necessary equipment. However, the benefits of this integration are obvious, including improved efficiency in natural resource management and increased capacity to respond to environmental threats.

Data acquired from site surveys provide an essential resource to various exploration and production activities, such as field development planning, well positioning, and subsea equipment positioning. For many years, site survey data has been delivered as a Computer-aided Design (CAD) product. As operators move into a Geographic Information Systems (GIS) environment, shortfalls from CAD-based workflows have become obvious. This paper presents a method of managing site survey data based on geo-information management principles and practices. The methodology presented in this paper defines a GIS data model for seabed survey data interpretation that satisfies GIS query, presentation, and data exchange purposes. This paper also defines a standard for GIS seabed survey data deliverables from contractors to operators, including methods and processes to move the model to/from CAD-based applications. The implementation of the data model has proven to be a relative success and allows for a better integration of subsurface applications internally. The results have helped define an efficient contractor-operator data exchange process by eliminating data ambiguity and enabling seamless GIS data transfer. Results have also produced a more intelligent seabed survey GIS dataset with rules and behaviors capability offered by an ArcGIS Geodatabase embedded into the data model. Use of a standard GIS data model and a standard list of GIS data deliverables required from contractors to operators will allow project teams to fully embrace the power of GIS data integration and allow nearly seamless and lossless movement of CAD/GIS representations of the model. Introduction In order to reach maximum potential benefit of data provided from seabed site surveys throughout the E & P lifecylcle (Figure 1), there is a need to ensure that data delivered from survey contractors to operators is done so in a structured, efficient, and consistent manner.

The work herein provides a general discussion on the issues involved in urban and landscape planning, with an emphasis on the visualization. The paper reviews some of the challenges involved in the planning using visualization tools and Geographic Information Systems. The purpose is to present a motivation to think about choices users make while applying methods of geoprocessing analysis for, somehow, most of users just apply tools but do not question their meaning. A proper conceptual base is a way to avoid following new tools without really contributing to new methodologies and not using the real potential of Geographic Information Systems. We have been living a new paradigm on urban planning, changing from authorial design to the decoding of collective values, using parametric modeling to understand, define and control values and expectations in urban land use. The responsibility of making urban proposals clear has as purpose not only making citizens understand plans, but also to instruct them on an urban common landscape, which is the role of the visualization. It discusses concepts on visualization and presents a methodological proposal to transform representation from absolute into relative in order to support the decision-making in urban landscapes. The paper herein presents techniques to construct a diagnosis of potentials and limitations, to propose maximum constructive envelopes for plots’ occupation, and to promote the visualization in order to share the decision with the community, mostly on the Parametric Modeling of Urban and Natural Landscape approach.

Although many geographical information systems (GISs) are very advanced in data processing and display, current GIS are not capable of physically based modelling. Especially, simulating transport of water and pollutants through landscapes is a problem in a GIS environment. A number of specific routing methods are needed in a GIS for hydrologic modelling, amongst these are the numerical solutions of the Saint-Venant equations, such as the kinematic wave approximation for transport of surface water in a landscape. The PCRaster Spatial Modelling language is a GIS capable of dynamic modelling. It has been extended recently with a kinematic wave approximation simulation tool to allow for physically based water flow modelling. The LISFLOOD model is an example of a physically based model written using the PCRaster GIS environment. The LISFLOOD model simulates river discharge in a drainage basin as a function of spatial data on topography, soils and land cover. Although hydrological modelling capabilities have largely increased, there is still a need for development of other routing methods, such as a diffusion wave. Copyright © 2000 John Wiley & Sons, Ltd.