Speed-related traffic accident analysis using GIS-based DBSCAN and NNH clustering

To ensure road safety and reduce traffic accidents, it is essential to determine geographical locations where traffic accidents occur the most. Spatial clustering methods of hot spots are used very effectively to detect such risky areas and take precautions to minimize or even avoid fatal or injury accidents. This study aims to determine speed-related hot spots in the pilot Mersin Region, which includes seven cities in the central-southern part of Turkey. Two different hot spot clustering methods, the Nearest Neighbourhood Hierarchical Clustering Method (NNH) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Method, were employed to analyse traffic accident data between 2014-2021, obtained from the General Directorate of Highways. CrimeStat III program, which is free software, was used to detect NNH clusters, while the DBSCAN clusters were obtained using the open-source GIS program QGIS, which was also used to visualize and evaluate the results comparatively. As a result of the analysis, it was determined which method gave more effective results in determining the traffic accident risk clusters. These clusters were analysed based on road geometries (intersection or corridors). In addition, by considering the areas where repeated accidents have occurred over the years, future predictions of traffic accidents have been estimated

Forecasting future climate boundary maps (2021–2060) using exponential smoothing method and GIS

Future-oriented forecasts have an important place in making forward-looking decisions and planning. At the beginning of these studies is the monitoring and detection of climate change. The climate is very variable. Therefore, by making predictions about the climate, preliminary information about how and to what extent the climate will change can be obtained, and accordingly, necessary precautions can be taken quickly. This study aims to produce predictive climate boundary maps using Geographic Information Systems (GIS), in which climate classification methods and time series methods are evaluated to monitor and determine the changes caused by the climate in 13 selected provinces in Turkey. The triple exponential smoothing method and the Erinc climate classification method were discussed. The data were obtained from the General Directorate of Meteorology (GDM) between 1930 and 2020, and each year's precipitation efficiency index (Im) of the Erinc climate classification method was calculated. It is divided into two classes for forecasting and testing current indices: test Im indices (1930–2014) and forecast test Im indices (2015–2020). MAD, MSE, and MAPE criteria were calculated to determine whether the Im estimates were meaningful. However, the accuracy of the estimates was ensured by considering the MAPE criteria for this study. After this stage, the analyses were performed again with test Im indices (1930–2020) and forecast Im indices (2021–2060), and Im indices predictions for the future were made. Finally, the obtained forecast indices were subjected to GIS interpolation analyses (Kriging and IDW), and future climate boundary maps were produced. Thanks to the outputs obtained from the study, how the climate classes of any region will be in the future and to what extent they will change will be provided by evaluating the climate classification and time series methods together. It will contribute to different studies in this field with its innovative analysis approach

Producing climate boundary maps using GIS interface model designed with Python

Climate and its effects need to be examined within a more planned and comprehensive framework to prevent the unfavorable impact of climate change. Thus, climate effects on the ecosystem can be identified by determining the geographical boundaries of different climate types. The Koppen, Trewartha, Thornthwaite, Erinc, Aydeniz, De Martonne, and De Martonne-Gottman methods are used in the classification of climates. These methods enable the regional differences of climate types to be determined and their changes over the years to be examined. A number of studies examining climate classes have produced graphic findings and maps. The absence of new approaches has resulted in climate classifications still being carried out via manual studies. However, a program for identifying and representing these methods in a convenient, fast, and automated way could facilitate the completion of analyses in a shorter time. The programming languages developed in recent years have made it easy to design interface models that can perform analyses faster and easier than prolonged manual methods. In this study, a climate boundary determination interface model, designed using the Python programming language, was developed for use in the ArcGIS 10.6 program to determine geographical climate boundaries automatically. The provinces of Artvin, Ordu, Rize, Trabzon, Giresun, Bayburt, and Samsun (Turkey) were chosen as the study area to test the interface model. The resulting interface model design is expected to: (1) address the dimensions of climate change in Intergovernmental Panel on Climate Change studies; (2) identify the climate changes in our country as an objective of the National Climate Change Strategy; and (3) determine the land-use changes caused by climate boundaries and examine the ownership dimension of the adaptation process in the declaration published by the International Geodesy Federation in 2014

Multicriteria decision and sensitivity analysis support for optimal airport site locations in Ordu Province, Turkey

ABSTRACTIn the study carried out in the Ordu province of Turkey, 16 criteria to be used in airport site selection were handled and evaluated by subjecting them to successive processes in the GIS environment. Each criterion was weighted with the AHP method, and a map of suitability for airport site selection was obtained in the GIS environment using these weights. The most suitable place for the airport in Ordu province was detected by evaluating the nine regions determined according to the resulting map. Then, the alternative areas preferred from the most suitable areas were evaluated according to the total scores from the classification intervals with a scenario where the criterion weights were assumed to be equal. Finally, sensitivity analysis was performed to identify those who played an active role in the site selection analysis or not. Thus the sensitivity of the site selection analysis was tested

Odor-aided analysis for landfill site selection: study of DOKAP Region, Turkey

In our world, with the increase of factors such as the rapid and irresponsible consumption of natural resources, man-made environmental disasters, global warming, and pollution of water resources in our world, the need for more efficient storage and disposal of solid waste has arisen. The presentation of the data required to solve spatial problems such as storage, management, and location selection can be carried out extensively and effectively using geographic information systems (GIS). On the other hand, the unsatisfactory results obtained with GIS recently have made it mandatory to use spatial multiple criteria decisionmaking (S-MCDM) methods that include the decision-makers in the process. In this study, landfill site selection was carried out in eight provinces in the region under the responsibility of the Eastern Black Sea Project Regional Development Administration (DOKAP). GIS and S-MCDM were used together in this site selection process. A total of eight spatial data layers were used in the site selection application. Afterwards, storage areas determined as suitable via GIS analysis underwent additional evaluation, taking into account geological, seismic, and environmental factors as well as transportation costs. In addition to these multicomponent evaluations, odor analyses were carried out on the proposed storage areas using the prevailing wind direction