Development of Web Based Road Accident Data Management System in GIS Environment: a Case Study

Creation of a consistent 3D model of a city requires accurate data. Usually, accuracy assurance problems of data are solved by time consuming and expensive process of collecting and aligning with ground control points (GCP). Therefore, alternative methods become important. Using existing Geographic Information Systems (GIS) databases may decrease the time and cost of creating a reference dataset by reducing the number of GCPs required for producing high quality 3D data or GIS databases can serve as reference data. For this purpose, new spatial data analysis methods are needed to assure that GIS databases are of high-quality. In this paper, we propose a novel methodology and its sample development for comparative spatial analysis of digitized point cloud and the corresponding GIS database in order to statistically assess opportunities to align Mobile Mapping Systems (MMS) data with existing GIS databases or to improve involved datasets. The method is evaluated using LiDAR data provided by Estonian company Reach-U Ltd. and GIS database layers from different Estonian open and closed databases.

This study aims to create an integrated geographical information system (GIS) database of natural resources represented by mining activities in the Red Sea area in Sudan. GIS is a vital tool to help the decision‐makers in managing and classifying these resources in terms of quantity and quality within the concept of sustainable development. The paper extracts some models of investment map indicators. In addition to that, it conducts a study and research aimed at developing a mineral resources management and discovering and identifying the new areas of mineral wealth in GIS database. The motivation of this study stems from the fact that countries with these types of wealth can greatly grow their gross domestic product (GDP) through the optimum management and governance. Most of the least development countries (LDC) can focus on the search for alternative natural resources (beside other conventional resources, i.e., agriculture) to support their economy. Red Sea region is one of the great regions full of natural and mineral resources (specifically gold and minerals), beside its strategic location on the way of most of the global trade transaction path. This paper designs and implements GIS multiple database layers. The multiple layers were modelled to overcome the technical difficulties that result from processing of large quantities of tempospatial information. The database was built by using an Oracle database system due to its capability of multilayer design.

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.

As the research hotspot of intelligent transportation system (ITS), unmanned ground vehicle (UGV) with high intelligence is able to reduce the probability of traffic accidents and traffic congestion, and improve the efficiency of urban transportation system. Environmental perception is one of the key research topics in the research of key technologies of UGV. The campus transportation environment, as a simple version of the urban transportation environment, needs to perceive as much traffic information as the urban traffic environment. At the same time, the geographic information system (GIS) has capabilities of strong geographical information storage and management, which can play a critical role in the environmental perception of UGV. In this paper, we study the autonomous driving of unmanned ground vehicle in the campus traffic environment, and the construction method of GIS database is proposed for the perception demand of UGV in the traffic environment of campus. Then, according to the GIS database, autonomous navigation control of UGV is carried out to verify its applicability and effectiveness. After testing, the GIS database proposed in this paper can assist the autonomous driving of UGV well and complete the driving tasks in the campus environment.

The environmental impact of a hazardous material spill is a complex function of the material's physical and chemical characteristics and the local environmental conditions in which it is spilled. This study develops a geographical probability distribution for two important environmental parameters affecting this impact: soil type and groundwater depth. The paper assesses the probability of exposure of various soil types and groundwater depth regions to hazardous material spills from railroad tank car accidents. The State Soil Geographic database (STATSGO), a geographic information system (GIS) database from the U.S. Department of Agriculture, was used to analyze soils, and real-time groundwater data from the U.S. Geological Survey were used to analyze groundwater. The “rail2m” GIS database available from the Bureau of Transportation Statistics was used to conduct overlay analyses with the soil GIS data to obtain the probability of occurrence of various soil types under rail lines. The same method could not be used for groundwater exposure because no suitable GIS database for this parameter was available. Therefore, a nationwide probability distribution of the groundwater depth ranges was developed. The proportion of the three soil types under rail lines was 44.0% for sand, 51.2% for silt, and 4.0% for clay, the remainder being over surface water. The probability of occurrence of five groundwater depth regions was 16.4% for 5 to 15 ft, 12.6% for 15 to 25 ft, 23.3% for 25 to 75 ft, 12.0% for 75 to 125 ft, and 35.9% for greater than 125 ft. A matrix of the probability distribution of environmental scenarios was developed; it is suitable for use in conducting environmental risk assessments of railroad transportation of hazardous materials.

Over the past two decades, the use of larger, deeper draft container ships has resulted in a need to provide deeper channels and berths to accommodate these larger vessels. In January 1998, the Port of Los Angeles (POLA) approved the Main Channel Deepening Program to deepen the Main Channel and associated channels and turning basins from the existing Elevation (El.) -45 feet MLLW (mean lower low water) to El. –50 feet MLLW. Since approval of that project, new vessels in the world container fleet and ship orders indicated that container vessels with a draft of 52 feet were being planned. In order to meet the need for deeper-draft vessels and in cooperation with the United States Army Corps of Engineers, POLA examined the feasibility of dredging the navigational channels within the Port of Los Angeles to as deep as El. –55 feet MLLW. The dredged materials from the new Channel Deepening Project will allow POLA to pursue future expansion and terminal development. A case history is presented herein that demonstrates integration of geophysical survey and geotechnical exploration data that was utilized to characterize dredge material, interpret subsurface stratigraphy, and determine the extent of structurally unsuitable fine-grained deposits. This integration was implemented by entering subsurface geophysical survey and geotechnical exploration data into a Geographic Information System (GIS) database. In addition to assisting in the site characterization, the GIS database has been used to determine limits of dredge units, create stratigraphic surfaces and subsurface cross sections, and to provide an estimate of the quantities of suitable material for strategic placement within the fill areas.

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.

The California Geological Survey (CGS) has completed a geographic information system (GIS)–based compilation of geologic maps of Quaternary age and older deposits in southern California. The compilation provides a consistent classification of surficial deposits within a 10 county area of interest to the Department of Water Resources (DWR) and the governor9s Alluvial Fan Task Force (AFTF). During the past two decades, rapid population growth in southern California has resulted in increased pressure for residential development on alluvial fans. Due to unique flood hazards associated with alluvial fans, the AFTF reviewed the state of knowledge regarding alluvial fan floodplains and developed recommendations specific to floodplain management. The CGS compilation was designed as a regional-scale planning tool to assist DWR, the AFTF, and local communities in evaluating hazards and locating future development on alluvial fans. The goal of the compilation was to represent southern California9s complex geology in a consistent user-friendly format that depicts areas of most recent alluvial fan deposition on a regional scale. The Derivative GIS Database merges new mapping and existing digital geologic data into a common seamless format that differentiates alluvial fan deposits, related Quaternary deposits, and various older deposits into 40 derivative units at a scale of 1: 100,000 for the entire mapped area. Original source data compiled at various scales and detail are retained in the Source GIS Database.

This project aims to develop a comprehensive web-based road accident data management system tailored for Udaipur City, Rajasthan, India, utilizing GIS technology. With increasing urbanization and traffic congestion, effective management of road accident data becomes paramount for improving road safety measures. The system will offer tools for efficient collection, storage, analysis, and visualization of accident data, empowering authorities to make evidence-based decisions. Following a structured methodology encompassing data collection, system design, development, testing, and implementation, the project holds great promise for reducing accident rates and enhancing public safety in Udaipur City. Similarly, a user-friendly accident data management system has been developed for selected locations of Surat City, Gujarat, India, utilizing GIS technology. Designed to be accessible to those unfamiliar with computers, the system facilitates accident data reporting and statistical analysis, aligning with Indian Roads Congress guidelines. By highlighting the necessity of GIS database and user-friendly systems for effective transportation planning, design, and administration, this initiative underscores the importance of data-driven decision-making in enhancing road safety measures

Given the frequent socioeconomic, political and concomitant ecological failures of science-driven marine protected area (MPA) programmes, it is now important to design MPAs by integrating natural and social science research more comprehensively. This study shows how indigenous peoples assisted in the design of MPAs by identifying marine substrates and related resident taxa on aerial photos, information which was then incorporated into a geographical information system (GIS) database, along with dive survey data. Two questions were asked: (1) Is indigenous ecological knowledge accurate enough for mapping the benthos and associated taxa? (2) Is such an approach an appropriate way for assisting in the biological and social design of MPAs in Oceania? Conventional quadrat field dive surveys were used to measure the accuracy of substrate identification by local informants and a visual survey was used to test hypotheses formulated from local knowledge regarding the spatial distribution and relative abundance of non-cryptic species within certain benthic habitats. Equivalence rates between indigenous aerial photo interpretations of dominant benthic substrates and in situ dive surveys were 75–85% for a moderately detailed classification scheme of the benthos, which included nine locally-defined abiotic and biotic benthic classes for the MPA seabed. Similarly, the taxa inventory showed a strong correspondence between the qualitative predictions of local fisherfolk and the quantitative analysis of non-cryptic species distribution, including their relative abundance and geophysical locations. Indigenous people's predictions about the presence or absence of fish in different benthic habitats corresponded 77% and 92% of the time (depending on scoring schema) with in situ visual measurements. These results demonstrate how incorporating local knowledge of benthic heterogeneity, existing biological communities, and particular spatio-temporal events of biological significance into a GIS database can corroborate the production of scientifically reliable base resource maps for designing MPAs in an environmentally and culturally sound fashion. This participatory approach was used to design and then establish MPAs in the Roviana and Vonavona region of the Western Solomon Islands. Under appropriate conditions, interdisciplinary work can complement the design of scientific fishery management and biodiversity conservation prescriptions for coastal Oceania.

Slope stability management systems (SSMSs) have been developed by multiple state transportation agencies to assess landslides adjacent to highways and aide in the effective allocation of resources for slope and/or roadway repairs. SSMSs catalog and analyze slope failures through the use of three main components: a landslide data collection system, a geographic information systems (GIS) database, and a hazard prioritization system. The components form a landslide management system used for the identification or prediction of landslide risk areas and the determination of landslide hazards to motorists. The Alabama Department of Transportation (ALDOT) has not previously employed a SSMS, which limits the ability of ALDOT engineers and geologists to effectively manage landslide hazards along highways. This paper discusses the development of a landslide collection system and database developed to improve the ability of ALDOT to identify common causes and locations of slope failures along Alabama highways. The collected data were converted into a GIS database, which allows for correlations between landslides and additional spatial data, such as average precipitation and surface geology. This paper presents a brief review of similar SMSSs, reviews the proposed database, and discusses preliminary observations. The data collected from this study can be used to identify common causes of landslides along Alabama highways and select efficient remediation options.

We have estimated methane (CH4) emission from total rice (Oryza sativa L.) paddies in Japan by means of a process-based biogeochemistry model, DeNitrification-DeComposition(DNDC)-Rice, combined with a geographic information system (GIS) database of climate, soil and farming practices. In the GIS database, 2 million ha of rice paddies were divided into 17,408 units according to 136 climate areas, 16 soil types, four classes of drainage rate and two classes of groundwater level, to simulate CH4 flux from each of the units applying the DNDC-Rice model. As a result, the national-scale CH4 emission in 1990 was estimated to be 216 Gg carbon (C), 13% lower than a previous inventory estimated by the Tier 2 method. By our Tier 3 approach, a relatively higher CH4 flux was estimated from eastern regions than from western regions of Japan, presumably due to the differences in climate and water management. Sensitivity analysis and uncertainty assessment indicated that it is important to account for the heterogeneity in soil properties such as field water capacity, iron (Fe) concentration and drainage rate, in order to reduce the uncertainty in regional estimates.

Abstract. In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM), radial-basis-function Fisher discriminant analysis (RBFDA), and a geographic information system (GIS) database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.

Potential of geographic information systems in building cost estimation and visualization

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.