Analysis of Fatal and Injury Traffic Accidents in Istanbul Sarıyer District with Spatial Statistics Methods

Traffic accidents, which continue to increase from year to year in Turkey and in the world, have become a huge problem that can result in serious traumas, injuries, and deaths, as well as their material and moral consequences. Many studies have been carried out in the world and in Turkey to reduce the number of traffic accidents, but these studies have not been very effective in reducing accidents. In this study, 3105 fatal or injured traffic accidents between 2010–2017 in Sarıyer district of Istanbul, Turkey’s largest city in terms of population, were discussed. We analyzed the statistical information on the subject in detail within the framework of geographic information systems. It has been tried to determine the sections where traffic accidents are concentrated in this region with studies based on spatial methods. Thematic accident map was created according to the accident types. In this context, the advantages and disadvantages of these methods were compared using Point Density, Kernel Density, Getis Ord Gi*, and Anselin Local Moran’s I (LISA) Spatial Autocorrelation. In addition, in order to observe the change in accidents, thematic accident and Kernel Density maps were created separately according to accident occurrence types in the beginning and last year. From this point of view, the changes that occurred in the accidents were interpreted. The current study determined that the most accidents were on some streets and these streets divided into regions in a plan. The cases were examined with statistical analyses according to accident types and using the Kernel Density method. In addition, it has been observed that Kernel Density method gives better visual results than other spatial methods. In this study, spatial analysis and statistical analysis methods were used to evaluate traffic accidents more realistically. The day of the week effect and month of the year effect on traffic accidents was investigated for the first time. In addition, it is proposed to bring a new approach to the prevention of traffic accidents by using hotspot, accident type, and day of the week effect.

Joint Modeling of Genetics and Field Variation in Plant Breeding Trials Using Relationship and Different Spatial Methods: A Simulation Study of Accuracy and Bias

Modelling field spatial patterns is standard practice for the analysis of plant breeding. Jointly fitting the genetic relationship among individuals and spatial information enables better separability between the variance due to genetics and field variation. This study aims to quantify the accuracy and bias of estimative parameters using different approaches. We contrasted three settings for the genetic term: no relationship (I), pedigree relationship (A), and genomic relationship (G); and a set of approaches for the spatial variation: no-spatial (NS), moving average covariate (MA), row-column adjustment (RC), autoregressive AR1 × AR1 (AR), spatial stochastic partial differential equations, or SPDE (SD), nearest neighbor graph (NG), and Gaussian kernel (GK). Simulations were set to represent soybean field trials at F2:4 generation. Heritability was sampled from a uniform distribution U(0,1). The simulated residual-to-spatial ratio between residual variance and spatial variance (Ve:Vs) ranged from 9:1 to 1:9. Experimental settings were conducted under an augmented block design with the systematic distribution of checks accounting for 10% of the plots. Relationship information had a substantial impact on the accuracy of the genetic values (G > A > I) and contributed to the accuracy of spatial effects (30.63–42.27% improvement). Spatial models were ranked based on an improvement to the accuracy of estimative of genetic effects as SD ≥ GK ≥ AR ≥ NG ≥ MA > RC ≥ NS, and to the accuracy of estimative of spatial effects as GK ≥ SD ≥ NG > AR ≥ MA > RC. Estimates of genetic and spatial variance were generally biased downwards, whereas residual variances were biased upwards. The advent of relationship information reduced the bias of all variance components. Spatial methods SD, AR, and GK provided the least biased estimates of spatial and residual variance.

Non-Stationary Modeling of Microlevel Road-Curve Crash Frequency with Geographically Weighted Regression

Vehicle crashes on roads are caused by many factors. However, the influence of these factors is not necessarily homogenous across locations, which is a challenge for non-stationary modeling approaches. To address this problem, this paper adopts two types of methods allowing parameters to fluctuate among observations, that is, the random parameter approach and the geographically weighted regression (GWR) approach. With road curvature, curve length, pavement friction, and traffic volume as independent variables, vehicle crash frequencies are modeled by two non-spatial methods, including the negative binomial (NB) model and random parameter negative binomial (RPNB), as well as three spatial methods (GWR approach). These models are calibrated in microlevel using a dataset of 9415 horizontal curve segments with a total length of 1545 kilometers for a period of three years (2016–2018) over the State of Indiana. The results revealed that the GWR approach can capture spatial heterogeneity and therefore significantly outperforms the conventional non-spatial approach. Based on the Akaike Information Criterion (AICc), geographically weighted negative binomial regression (GWNBR) was proved to be a superior approach for statewide microlevel crash analysis.

Valuation of harvested goods in Mida Creek with application of the TESSA approach

Mangroves are considered a highly productive blue forests resource providing services that are important to the community both locally and globally. In recent times there has been an increase in studies on valuation of ecosystem services provided by mangroves. However, there is need to provide a simplified approach to identify, assess and quantify ecosystem services. In this study the Toolkit for Ecosystem Services Site-based Assessment (TESSA) was used to assess the value of harvested goods provided by the mangroves of Mida Creek in the current state and under plausible alternative scenarios. Spatial methods (GIS) were used to collect data for the period 1985-2019, and household interviews were used to collect data on harvested goods. Descriptive statistics were used to summarize quantitative data. Results show that the estimated current annual value of harvested goods in Mida Creek is US$ 11.2 million. This value increased to US$ 14.3 million under the conservation scenario and reduced to US$ 10.9 million under the business as usual scenario (BAU). These findings add to the growing literature on ecosystem service valuation and the need to use site-specific non-modelling tools like TESSA.

Photovoltaics in Austria - open space and rooftop potential analysis on a high spatial resolution

<p>Austria aims to meet 100% of its electricity demand from domestic renewable sources by 2030 which means, that an additional 30 TWh per year are required. Solar energy will play a significant role to reach this goal, meaning the need for a substantial increase in photo-voltaic capacity. While some federal states and municipalities released a solar roof-top cadastre, there is lacking knowledge on the estimation of the potential of both, open space installations and roof-top modules, on a national level with a high spatial resolution. Results show significant differences between urban and rural areas, as well as between the Alpine regions and the Prealpine- and Easter Plain areas.</p><p>The work includes a framework to automatically process solar PV data and land-use data was developed and openly available for usage. The framework is able to fetch solar data automatically from a defined source, and join, manipulate and alter it with geodata applying various spatial methods.</p>