No abstract

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, solid wastes have to be stored or disposed of more effectively. 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 decision-making (S-MCDM) methods that include the decision-makers in the process. In this study, RSWSA site selection was carried out in eight cities under the responsibility of the Eastern Black Sea Project Regional Development Administration (DOKAP). A combination of GIS and S-MCDM was used in this site selection process. A total of eight 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.

Forests are essential in contributing to the continuity of the natural balance. Therefore, their protection and sustainability are vital. However, all over the world, forest fires occur, and forests are destroyed due to both human factors and unknown causes. It is necessary to carry out studies to prevent this destruction. At this point, GIS‐based location–time relationship‐based hot spot clustering analysis can provide significant advantages in detecting risky spots of forest fires. In this study, GIS‐based emerging hot spot clustering analysis was carried out to determine the risky areas where forest fires will occur and to carry out preventive studies in the relevant areas. Turkey was chosen as the pilot region, and analyses were carried out using the data obtained from the official statistics of the Ministry of Agriculture and Forestry General Directorate of Forestry according to the causes of the fires (negligence, intentional, accidental, unknown cause and natural) between the years 2010 and 2020. Spatial autocorrelation analysis was conducted for each fire type, and threshold distances were determined {with a number of distance bands = 20,000, distant increment = 10,000}. Emerging hot spot analyses were then conducted, and the results were presented as maps and statistical outputs. According to all fire types, 15 new hot spots, 14 persistent hot spots, 33 sporadic hot spots, 9 consecutive hot spots, 15 intensifying, and 2 diminishing hot spot regions were obtained throughout the country.

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 Köppen, 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.