Predictive model for areas with illegal landfills using logistic regression

被引:14
|
作者
Luis Lucendo-Monedero, Angel [1 ]
Jorda-Borrell, Rosa [1 ]
Ruiz-Rodriguez, Francisca [1 ]
机构
[1] Univ Seville, Dept Phys Geog & Reg Geog Anal, Seville, Spain
关键词
municipal system waste management; construction and demolition waste (C&DW); illegal landfill; awareness-raising campaigns; topography; logistic regression; SOLID-WASTE MANAGEMENT; DEVELOPING-COUNTRIES; SITE SELECTION; CHALLENGES; GIS; MULTICRITERIA; DISPOSAL; CITIES;
D O I
10.1080/09640568.2014.993751
中图分类号
F0 [经济学]; F1 [世界各国经济概况、经济史、经济地理]; C [社会科学总论];
学科分类号
0201 ; 020105 ; 03 ; 0303 ;
摘要
The existence of illegal landfills is an environmental problem in most countries. However, research on this issue is scarce and limited by the availability and quality of data on the subject. Thus, most illegal landfill studies have only been conducted in a partial manner, focusing on geographical aspects or the causes of these landfills (lack of environmental awareness, inadequate waste management systems, and the role of local government). This research analyses a sample of 120 possible areas with illegal landfills in Andalusia using logistic regression in order to obtain a predictive model for the occurrence of these landfills, including both types of variables (geographical and behavioural) jointly. The results confirm that the variables that most influence the occurrence of illegal landfills are spatial ("Industrial Land", "Plains" and "Rural Land"); whilst the variables that most reduce the likelihood of illegal landfills are those related to certain characteristics of the municipal waste management system and environmental awareness, such as "Availability of Recycling Facilities", "Punitive Policies", "Supervision" and "Awareness-raising Campaigns". The model obtained shows that variables of very different nature and magnitude interact in the occurrence of illegal landfills, each of which contributes a series of features characteristic of its scale. It is advisable, therefore, to perform an analysis using a multi-scale approach in order to gain an overall understanding of the phenomenon.
引用
收藏
页码:1309 / 1326
页数:18
相关论文
共 50 条
  • [41] Logistic Regression Trust-A Trust Model for Internet-of-Things Using Regression Analysis
    Solomon, Feslin Anish Mon
    Sathianesan, Godfrey Winster
    Ramesh, R.
    COMPUTER SYSTEMS SCIENCE AND ENGINEERING, 2023, 44 (02): : 1125 - 1142
  • [42] Logistic regression model for predicting the failure probability of a landslide dam
    Dong, Jia-Jyun
    Tung, Yu-Hsiang
    Chen, Chien-Chih
    Liao, Jyh-Jong
    Pan, Yii-Wen
    ENGINEERING GEOLOGY, 2011, 117 (1-2) : 52 - 61
  • [43] Variable and threshold selection to control predictive accuracy in logistic regression
    Kuk, Anthony Y. C.
    Li, Jialiang
    Rush, A. John
    JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 2014, 63 (04) : 657 - 672
  • [44] Predictive Performance of Logistic Regression for Imbalanced Data with Categorical Covariate
    Abd Rahman, Hezlin Aryani
    Wah, Yap Bee
    Huat, Ong Seng
    PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY, 2021, 29 (01): : 181 - 197
  • [45] A Predictive Logistic Regression Equation for Neck Pain in Helicopter Aircrew
    Harrison, Michael F.
    Neary, J. Patrick
    Albert, Wayne J.
    Croll, James C.
    AVIATION SPACE AND ENVIRONMENTAL MEDICINE, 2012, 83 (06): : 604 - 608
  • [46] Predictive Performance of Logistic Regression for Imbalanced Data with Categorical Covariate
    Abd Rahman, Hezlin Aryani
    Wah, Yap Bee
    Huat, Ong Seng
    PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY, 2020, 28 (04): : 1141 - 1161
  • [47] Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models
    Biswajeet Pradhan
    Saro Lee
    Environmental Earth Sciences, 2010, 60 : 1037 - 1054
  • [48] Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models
    Pradhan, Biswajeet
    Lee, Saro
    ENVIRONMENTAL EARTH SCIENCES, 2010, 60 (05) : 1037 - 1054
  • [49] Prediction of Rainfall Using Logistic Regression
    Imon, A. H. M. Rahmatullah
    Roy, Manos C.
    Bhattacharjee, S. K.
    PAKISTAN JOURNAL OF STATISTICS AND OPERATION RESEARCH, 2012, 8 (03) : 655 - 667
  • [50] Landslide Susceptibility Mapping by Using Logistic Regression Model with Neighborhood Analysis: A Case Study in Mizunami City
    Wang, Liangjie
    Sawada, Kazuhide
    Moriguchi, Shuji
    INTERNATIONAL JOURNAL OF GEOMATE, 2011, 1 (02): : 99 - 104