The usage of OpenStreetMap (OSM), one of the resources offered by Volunteered Geographic Information (VGI), has rapidly increased since it was first established in 2004. In line with this increased usage, a number of studies have been conducted to analyze the accuracy and quality of OSM data, but many of them have constraints on evaluating the profiles of contributors. In this paper, OSM road data have been analyzed with the aim of characterizing the behavior of OSM contributors. The study area, Ankara, the capital city of Turkey, was evaluated with several network analysis methods, such as completeness, degree of centrality, betweenness, closeness, PageRank, and a proposed method measuring the activation of contributors in a bounded area from 2007–2017. An evaluation of the results was also discussed in this paper by taking into account the following indicators for each year: number of nodes, ways, contributors, mean lengths, and sinuosity values of roads. The results show that the experience levels of the contributors determine the contribution type. Essentially, more experience makes for more detailed contributions.

When generalizing a group of objects, displacement is an essential operation to resolve the conflicts arising between them due to enlargement of their symbol sizes and reduction of available map space. Although there are many displacement methods, most of them are rather complicated. Therefore, more practical methods are still needed. In this article, a new building displacement approach is proposed. For this purpose, buildings are grouped and zones are created for them in the blocks via Voronoi tessellation and buffering. Linear patterns are then detected through buffer analyses and the respective zones are narrowed to be able to preserve these patterns. After all the buildings are displaced inside their zones, grid points are generated and then weighted through kernel density estimation and buffer analyses to find suitable locations. Accordingly, the buildings are displaced toward the computed locations iteratively. The proposed approach directly enforces minimum distance and positional accuracy constraints while several indirect mechanisms are used for preserving spatial patterns and relationships. For the quality evaluation of the displacement, the angle, length and shape comparison measures are introduced, computed based on the (Delaunay) triangles or the azimuth comparison measure of the connection lines, generated for the buildings. The quality evaluation criteria are yielded according to the visual assessment of the displacement quality and the quantitative analysis of the measures. The findings demonstrate that the proposed approach is quite effective and practical for zonal building displacement.

Accurate identification of plant species forms the basis of taxonomy, biodiversity assessment, and conservation planning. This requirement is especially urgent in arid ecosystems such as those of Saudi Arabia, where limited rainfall, fragile habitats, and high ecological stress create distinctive but poorly documented flora. In this study, a deep learning framework, termed PTL-Inception, was developed to classify desert plants and to provide reliable taxonomic data that can be integrated into biodiversity and phylogenetic studies. A dataset of ten native species was compiled and expanded through augmentation, and several state-of-the-art architectures were tested. InceptionV3 was found to be the most effective baseline, and the network was further modified by incorporating ten additional layers, transfer learning, and hyperparameter tuning. The proposed model achieved an accuracy of 99.46%, with precision and recall values of 99.46% and 99.44%, respectively. Reliability was confirmed through K-fold validation, while early stopping reduced training time with minimal loss of accuracy. Beyond these computational outcomes, the study demonstrates how deep learning can complement traditional taxonomy by producing consistent species-level identifications. The outputs can be combined with spatial and phylogenetic approaches to explore patterns of diversity, endemism, and adaptation in desert ecosystems, thereby supporting conservation strategies and biodiversity management.

Acil tıbbi destek tedavi kaynaklarına olan talep arzı aştığında acil servislerin kalabalıklaşmasına neden olmaktadır. Bu durum hem kırsal hem de kentsel izlenebilen bir durumdur ve büyük hastanelerin acil servislerin %90'ının tam kapasite veya kapasitesinin üstünde çalışmaktadır (1). Kalabalık ve kritik hastaları kabul eden acil servisler zaman zaman aşırı hasta yükü nedeniyle yoğunluklarını acil sağlık hizmetleri koordinatör merkezlere bildirerek hasta kabul etmemekte bu dönemde ambulans ile gerilen hasta başka merkezlere yönlendirilmektedir. Bu durum hastalar için zaman kaybına sağlık yönetimi için ise kaynak kaybına neden olmaktadır

The year 2023 represents a 100-year period of becoming a country for Türkiye. This study examines the urban sprawl evolution of Konya in Turkey over a century of urbanization. The sprawl process is assessed through the analysis of urban plans from 1923, 1945, 1967, 2000, and 2023 using (1) density calculation derived from the population divided by built-up cells and (2) density calculation utilizing Inverse S function based on land use data. Considering the calculated densities, the city's evolution over a hundred years is examined concerning evolving regulations land use decisions, and growth trajectories. The existing conditions, encompassing both the advantageous and disadvantageous facets of urban sprawl, are examined using SWOT analysis, and spatial development strategies for the future are devised. As a result, it has been determined that Konya has undergone urban sprawl in certain directions irregularly, attributed to transportation-focused planning decisions, deviating from human-scale and compact urban development.