AbstractThis study focuses on the geo-statistical assessment of spatial estimation models in forest crimes. Use'/> A study on the use and modeling of geographical information system for combating forest crimes: an assessment of crimes in the eastern Mediterranean forests
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A study on the use and modeling of geographical information system for combating forest crimes: an assessment of crimes in the eastern Mediterranean forests

机译:地理信息系统用于打击森林犯罪的研究和建模:对地中海东部森林犯罪的评估

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AbstractThis study focuses on the geo-statistical assessment of spatial estimation models in forest crimes. Used widely in the assessment of crime and crime-dependent variables, geographic information system (GIS) helps the detection of forest crimes in rural regions. In this study, forest crimes (forest encroachment, illegal use, illegal timber logging, etc.) are assessed holistically and modeling was performed with ten different independent variables in GIS environment. The research areas are three Forest Enterprise Chiefs (Baskonus, Cinarpinar, and Hartlap) affiliated to Kahramanmaras Forest Regional Directorate in Kahramanmaras. An estimation model was designed using ordinary least squares (OLS) and geographically weighted regression (GWR) methods, which are often used in spatial association. Three different models were proposed in order to increase the accuracy of the estimation model. The use of variables with a variance inflation factor (VIF) value of lower than 7.5 in Model I and lower than 4 in Model II and dependent variables with significant robust probability values in Model III are associated with forest crimes. Afterwards, the model with the lowest corrected Akaike Information Criterion (AICc), and the highestR2value was selected as the comparison criterion. Consequently, Model III proved to be more accurate compared to other models. For Model III, while AICcwas 328,491 andR2was 0.634 for OLS-3 model, AICcwas 318,489 andR2was 0.741 for GWR-3 model. In this respect, the uses of GIS for combating forest crimes provide different scenarios and tangible information that will help take political and strategic measures.
机译: Abstract 此研究的重点是森林犯罪中空间估算模型的地统计学评估。地理信息系统(GIS)广泛用于评估犯罪和与犯罪相关的变量,可帮助发现农村地区的森林犯罪。在这项研究中,对森林犯罪(森林侵占,非法使用,非法砍伐木材等)进行了全面评估,并在GIS环境中使用十个不同的独立变量进行了建模。研究领域是隶属于Kahramanmaras的Kahramanmaras森林区域局的三名林业企业负责人(Baskonus,Cinarpinar和Hartlap)。使用通常在空间关联中使用的普通最小二乘(OLS)和地理加权回归(GWR)方法设计了一个估计模型。为了提高估计模型的准确性,提出了三种不同的模型。在模型I中使用方差膨胀因子(VIF)值低于7.5且在模型II中使用低于4的变量以及模型III中具有显着稳健概率值的因变量与森林犯罪有关。之后,选择修正的Akaike信息准则最低的模型(AIC c ),并且具有最高的 R 2 值作为比较标准。因此,与其他模型相比,模型III被证明更加准确。对于模型III,OLS-3模型的AIC c 为328491,而 R 2 为0.634,而AIC 对于GWR-3模型,c 为318,489, R 2 为0.741。在这方面,使用GIS打击森林犯罪可提供不同的场景和有形的信息,有助于采取政治和战略措施。

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