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      Monitoring of UN sustainable development goal SDG-9.1.1: study of Algerian “Belt and Road” expressways constructed by China

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          Abstract

          The proportion of the rural population who live within 2 km of an all-season road is an indicator of the United Nations’ Sustainable Development Goals (SDGs) 9.1.1. This paper aims to calculate SDG indicator 9.1.1 in the proximity of five Algerian expressways. Three monitoring methods are proposed for different spatial regions based on the five expressways built by China’s Belt and Road Initiative Project. These methods are based on remote sensing and WorldPop and The High Resolution Settlement Layer (HRSL) population data. The results indicate that (1) the WorldPop population statistics show that the five expressways built by China’s Belt Project have increased the rural population of the 2 km buffer zone by 192,016 between the start of construction and eight years after its completion. By the end of 2019, the population increased by 329,291 accounting for 1.17% of the rural population. (2) Based on populations estimated form built-up index (NDBI) building areas, the rural populations within the 2 km buffer area of the Bejaia-Haniff Expressway in 2011, 2015, and 2019 were 273,118, 306,430, and 375,408, respectively. (3) HRSL population grid statistics indicate that, in 2015, the populations were: East-West Expressway = 911,549, Bejaia Expressway = 127,471, Tipaza Expressway = 71,411, North-South Expressway = 30,583, and Cherchell Ring Expressway = 41,657. (4) A visual interpretation method based on Google Earth imagery was used to count the number of buildings and number of building floors in the town of Tikhramtath. Based on the estimated population of each building and floor, the population of Tikhramtath town in 2011, 2015, 2017, and 2019 was estimated as 1,790, 2,785, 3,365, and 3,870, respectively. (5) Through analysis and accuracy assessment, the appropriate statistical methods for different regions were determined.

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          Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms

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            A new index for delineating built‐up land features in satellite imagery

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              Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery

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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                peerj
                PeerJ
                PeerJ Inc. (San Diego, USA )
                2167-8359
                2 June 2020
                2020
                : 8
                : e8953
                Affiliations
                [1 ]College of Earth Sciences, Chengdu University of Technology , Chengdu, Si chuan Province, China
                [2 ]State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences , Beijing, China
                [3 ]College of Tourism and Urban-Rural Planning, Chengdu University of Technology , Chengdu, Si chuan Province, China
                Article
                8953
                10.7717/peerj.8953
                7274168
                5d5f81fd-23f1-40c4-aa6d-c24a79015094
                ©2020 Jia et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 3 October 2019
                : 21 March 2020
                Funding
                Funded by: Strategic Priority Research Program of Chinese Academy of Sciences
                Award ID: XDA19030304
                Funded by: Youth Innovation Promotion Association
                Award ID: 2017089
                This work was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (No. XDA19030304) and the Youth Innovation Promotion Association CAS (No. 2017089). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Environmental Impacts
                Spatial and Geographic Information Science

                sdg,visual interpretation,ndbi,estimation method
                sdg, visual interpretation, ndbi, estimation method

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