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Measuring and Monitoring Progress towards SDGs by Integrating Geospatial and...
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Measuring and Monitoring Progress towards SDGs by Integrating Geospatial and Statistical Information

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 31919

Special Issue Editors

Prof. Dr. Jun Chen

E-Mail Website
Guest Editor
National Geomatics Center of China, 28 Lianhuachi West Road, Beijing 100830, China
Interests: geospatial data modeling and updating; spatial relation; global land cover mapping; SDGs monitoring
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Songnian Li

E-Mail Website
Guest Editor
Department of Civil Engineering, Toronto Metropolitan University, Toronto, ON, Canada
Interests: big geospatial data analytics; data mining and knowledge discovery; human mobility; spatio-temporal dynamics; citizen science; digital twins for smart cities
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yifang Ban

E-Mail Website
Guest Editor
Division of Geoinformatics and Department of Urban Planning and Environment at KTH Royal Institute of Technology in Stockholm, Stockholm, Sweden
Interests: EO big data analytics; multitemporal remote sensing; SAR-based classification and change detection; urban mapping and wildfire monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The United Nations 2030 Agenda defines 17 Sustainable Development Goals (SDGs) and 169 targets, aiming to end poverty, promote prosperity and people’s well-being while protecting the environment by 2030, and representing a new coherent way of embracing economic growth, social inclusion, and environmental protection as an “indivisible whole”. The United Nations further proposed and promoted a systematic follow-up and review of the implementation of this global agenda, including indicators and evidence-based reporting of the progress towards SDGs at national, regional, and global levels.

It was with this background that the First United Nations World Geospatial Information Congress (UN WGIC), held from 19–21 November 2018 in Deqing, China, had taken “Measuring and Monitoring SDGs” as one of its major six themes. Several plenary and parallel sessions were organized to present the latest development and case studies in this field. In particular, the results of a comprehensive measurement of progress towards 2030 SDGs in Deqing County using geospatial and statistical information were presented at the congress and released with an online knowledge portal. It demonstrated that the comprehensive progress towards SDGs in an entire administrative region can best be measured and assessed by integrating geospatial and statistical information.

The integrated utilization of geospatial and statistical information in measuring and monitoring progress towards SDGs presents a number of theoretical, technological, and operational challenges. More efforts should be devoted to this emerging research and application topic, including the exploration of new methodologies and algorithms, the development of monitoring systems and good practices, as well as the formulation of technical standards.

This Special Issue aims to examine these fundamental and operational issues related to the integrated utilization of geospatial and statistical information in measuring and monitoring progress towards 2030 SDGs. We cordially invite original research contributions on topics including, but not limited to, the following:

  • Concepts and models for integrating geospatial and statistical information in supporting 2030 SDGs;
  • Geospatial and statistical information-based comprehensive or thematic measurement and monitoring of progress towards 2030 SDGs at global/national or local levels;
  • Deriving SDGs indicator(s) through the integration of geospatial and statistical information;
  • Geospatial disaggregation and aggregation in supporting SDGs;
  • SDGs analysis and assessment with indicators and geospatial evidence;
  • Data quality and uncertainty issues in measuring and analyzing SDG indicators
  • Visualizing SDG indicators and SDGs progress from a geographical perspective;
  • Geospatial knowledge-supported SDGs service portal;
  • And more.

Papers must be original contributions, not previously published or submitted to other journals. Papers published or submitted for publication in conference proceedings may be considered if they are considerably extended and improved. Authors must use the provided Microsoft Word template or LaTeX template to prepare their manuscript, and must follow the Instructions for Authors at https://www.mdpi.com/journal/ijgi/instructions.

 

Prof. Jun Chen
Prof. Songnian Li
Prof. Yifang Ban
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • SDG indicators
  • geospatial and statistical integration
  • disaggregation and aggregation
  • measurement and monitoring
  • analysis, assessment and visualization
  • technical standards and guidelines

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Published Papers (6 papers)

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20 pages, 9701 KiB  
Article
Supporting SDG 15, Life on Land: Identifying the Main Drivers of Land Degradation in Honghe Prefecture, China, between 2005 and 2015
by Tuo Wang, Gregory Giuliani, Anthony Lehmann, Yangming Jiang, Xiaodong Shao, Liping Li and Huihui Zhao
ISPRS Int. J. Geo-Inf. 2020, 9(12), 710; https://doi.org/10.3390/ijgi9120710 - 27 Nov 2020
Cited by 13 | Viewed by 6069
Abstract
The essence of the 2030 Agenda for Sustainable Development adopted by the United Nations is described in 17 Sustainable Development Goals (SDGs). SDG 15 focuses on Life on Land, in other words, terrestrial biodiversity and ecosystems, as well as their services. Land degradation [...] Read more.
The essence of the 2030 Agenda for Sustainable Development adopted by the United Nations is described in 17 Sustainable Development Goals (SDGs). SDG 15 focuses on Life on Land, in other words, terrestrial biodiversity and ecosystems, as well as their services. Land degradation is a severe anthropic and natural phenomenon that is affecting land use/cover globally; therefore, a dedicated target of the SDG 15 (the indicator 15.3.1) was proposed. The identification of the areas where land degradation has occurred and the analysis of its drivers allow for the design of solutions to prevent further degradation in the studied areas. We followed the methodology proposed by the United Nations Convention to Combat Desertification (UNCCD) to study the land degradation in the Honghe Prefecture in southwest China between 2005 and 2015. Through spatial analysis, we found that the degraded areas were consistent with the areas of active human activities (such as urban centers), while the impact of natural factors (such as disasters) on land degradation existed in small areas at high altitudes. Land degradation was affected primarily by the loss of land productivity and secondly by land cover changes caused by the growth of artificial areas. Changes in the soil organic carbon were not significant. We concluded that human activity was the main driver of land degradation in Honghe Prefecture. Decision makers should work to find a balance between economic development and environmental protection to restore degraded land and strive to achieve a land degradation-neutral prefecture to defend all ecosystem services. Full article
(This article belongs to the Special Issue Measuring and Monitoring Progress towards SDGs by Integrating Geospatial and Statistical Information)
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17 pages, 8014 KiB  
Article
Quantitative Evaluation of Spatial Differentiation for Public Open Spaces in Urban Built-Up Areas by Assessing SDG 11.7: A Case of Deqing County
by Qiang Chen, Mingyi Du, Qianhao Cheng and Changfeng Jing
ISPRS Int. J. Geo-Inf. 2020, 9(10), 575; https://doi.org/10.3390/ijgi9100575 - 30 Sep 2020
Cited by 12 | Viewed by 3314
Abstract
Urban public open spaces refer to open space between architectural structures in a city or urban agglomeration that is open for urban residents to conduct public exchanges and hold various activities. Sustainable Development Goal (SDG) 11.7 in the 2030 UN Agenda for Sustainable [...] Read more.
Urban public open spaces refer to open space between architectural structures in a city or urban agglomeration that is open for urban residents to conduct public exchanges and hold various activities. Sustainable Development Goal (SDG) 11.7 in the 2030 UN Agenda for Sustainable Development clearly states that the distribution characteristics of public open spaces are important indicators to measure the sustainable development of urban ecological society. In 2018, in order to implement the sustainable development agenda, China offered the example of Deqing to the world. Therefore, taking Deqing as an example, this paper uses geographic statistics and spatial analysis methods to quantitatively evaluate and visualize public open spaces in the built area in 2016 and analyzes the spatial pattern and relationship of the population. The results show that the public open spaces in the built-up area of Deqing have typical global and local spatial autocorrelation. The spatial pattern shows obvious differences in different parts of the built area and attributes of public open spaces. According to the results of correlation analysis, it can be seen that the decentralized characteristics of public open spaces have a significant relationship with the population agglomeration, and this correlation is also related to the types of public open spaces. The assessment results by SDG 11.7.1 indicate that the public open spaces in the built-up area of Deqing conform to the living needs of residents on the whole and have a humanized space design and good accessibility. However, the per capita public open spaces of towns and villages outside the built area are relatively low, and there is an imbalance in public open spaces. Therefore, more attention should be paid to constructing urban public open spaces fairly. Full article
(This article belongs to the Special Issue Measuring and Monitoring Progress towards SDGs by Integrating Geospatial and Statistical Information)
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19 pages, 6153 KiB  
Article
A Comprehensive Measurement of Progress toward Local SDGs with Geospatial Information: Methodology and Lessons Learned
by Jun Chen, Shu Peng, Hao Chen, Xuesheng Zhao, Yuejing Ge and Zhilin Li
ISPRS Int. J. Geo-Inf. 2020, 9(9), 522; https://doi.org/10.3390/ijgi9090522 - 1 Sep 2020
Cited by 19 | Viewed by 4788
Abstract
The UN’s 2030 Agenda defined 17 Sustainable Development Goals (SDGs). In order to ensure the implementation of this global agenda, the UN proposed a systematic follow-up and review through indicator-based tracking and reporting of the progress with statistical and geospatial information toward SDGs [...] Read more.
The UN’s 2030 Agenda defined 17 Sustainable Development Goals (SDGs). In order to ensure the implementation of this global agenda, the UN proposed a systematic follow-up and review through indicator-based tracking and reporting of the progress with statistical and geospatial information toward SDGs at national, regional, and global levels. This has posed many technical and institutional challenges. Although international communities have devoted great attention to this hot topic, most of their work has focused on the conceptual design and preliminary testing. There are very few good practices for a comprehensive measurement and assessment of progress toward SDGs with the integration of statistical and geospatial information at national or local levels. This paper presents the methodology and results of a pioneer project which measured the progress toward SDGs at a local level in China (i.e., Deqing County) by integrating statistical and geospatial information. In this study, a number of technical/institutional issues have been tackled, such as the adoption of appropriate indicators at a local level, availability and acquisition of reliable data sets, and spatiotemporal analysis with a geographical perspective, interaction between SDGs and cross-sector coordination. The major conclusions are (a) the comprehensive progress toward SDGs in Deqing can be most appropriately measured and assessed by integrating geospatial and statistical information; (b) Deqing has made significant economic and social advances while maintaining a good ecological environment over the past few years. The results were released at the first United Nations World Geospatial Information Congress as a good practice and a live example to stimulate discussions. Full article
(This article belongs to the Special Issue Measuring and Monitoring Progress towards SDGs by Integrating Geospatial and Statistical Information)
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27 pages, 10424 KiB  
Article
Measuring SDG 15 at the County Scale: Localization and Practice of SDGs Indicators Based on Geospatial Information
by Shaoyang Liu, Jianjun Bai and Jun Chen
ISPRS Int. J. Geo-Inf. 2019, 8(11), 515; https://doi.org/10.3390/ijgi8110515 - 13 Nov 2019
Cited by 32 | Viewed by 7727
Abstract
To achieve the goal of worldwide sustainable protection and utilization of terrestrial ecosystems, it is necessary to quantitatively assess the implementation of Sustainable Development Goal 15 (SDG 15) at all administrative levels, especially at the grass-roots level, using the indicator framework of the [...] Read more.
To achieve the goal of worldwide sustainable protection and utilization of terrestrial ecosystems, it is necessary to quantitatively assess the implementation of Sustainable Development Goal 15 (SDG 15) at all administrative levels, especially at the grass-roots level, using the indicator framework of the UN SDGs. However, in the SDG 15 indicator system, the relationship between goal and indicators is ambiguous, and the results of the indicators cannot be visualized to show the differences within regions. Moreover, its design scale is country-oriented, which suggests that the indicator system cannot be applied directly to the county levels. In light of these issues, this paper used four modalities of localization to form an indicator system of localization, and applied it in the quantitative evaluation of meeting the objectives of SDG 15 in Deqing County, China. The localized indicator system for county level based on geospatial information included six indicators, which were clustered into three categories: sustainable forest management, halt and reverse land degradation, and conservation of biodiversity. By comparing and evaluating the quantitative results of SDG 15 in Deqing, 70% of the comparable indicators in the localization indicator system were at the forefront of those in China or the world. The results showed that grouped analysis of the targets and indicators could clarify the relationship between the implications of the goal and indicators, and the indicator system based on the geographic information was conducive to displaying the spatial distribution of the results of the indicators and clarifying the internal differences. Full article
(This article belongs to the Special Issue Measuring and Monitoring Progress towards SDGs by Integrating Geospatial and Statistical Information)
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17 pages, 5460 KiB  
Article
Geospatial Disaggregation of Population Data in Supporting SDG Assessments: A Case Study from Deqing County, China
by Yue Qiu, Xuesheng Zhao, Deqin Fan and Songnian Li
ISPRS Int. J. Geo-Inf. 2019, 8(8), 356; https://doi.org/10.3390/ijgi8080356 - 13 Aug 2019
Cited by 16 | Viewed by 4717
Abstract
Quantitative assessments and dynamic monitoring of indicators based on fine-scale population data are necessary to support the implementation of the United Nations (UN) 2030 Agenda and to comprehensively achieve its 17 Sustainable Development Goals (SDGs). However, most population data are collected by administrative [...] Read more.
Quantitative assessments and dynamic monitoring of indicators based on fine-scale population data are necessary to support the implementation of the United Nations (UN) 2030 Agenda and to comprehensively achieve its 17 Sustainable Development Goals (SDGs). However, most population data are collected by administrative units, and it is difficult to reflect true distribution and uniformity in space. To solve this problem, based on fine building information, a geospatial disaggregation method of population data for supporting SDG assessments is presented in this paper. First, Deqing County in China, which was divided into residential areas and nonresidential areas according to the idea of dasymetric mapping, was selected as the study area. Then, the town administrative areas were taken as control units, building area and number of floors were used as weighting factors to establish the disaggregation model, and population data with a resolution of 30 m in Deqing County in 2016 were obtained. After analyzing the statistical population of 160 villages and the disaggregation results, we found that the global average accuracy was 87.08%. Finally, by using the disaggregation population data, indicators 3.8.1, 4.a.1, and 9.1.1 were selected to conduct an accessibility analysis and a buffer analysis in a quantitative assessment of the SDGs. The results showed that the SDG measurement and assessment results based on the disaggregated population data were more accurate and effective than the results obtained using the traditional method. Full article
(This article belongs to the Special Issue Measuring and Monitoring Progress towards SDGs by Integrating Geospatial and Statistical Information)
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Other

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20 pages, 5248 KiB  
Technical Note
Combining UAV Imagery, Volunteered Geographic Information, and Field Survey Data to Improve Characterization of Rural Water Points in Malawi
by Marc van den Homberg, Arjen Crince, Jurg Wilbrink, Daniël Kersbergen, Gumbi Gumbi, Simon Tembo and Rob Lemmens
ISPRS Int. J. Geo-Inf. 2020, 9(10), 592; https://doi.org/10.3390/ijgi9100592 - 9 Oct 2020
Cited by 4 | Viewed by 3925
Abstract
As the world is digitizing fast, the increase in Big and Small Data offers opportunities to enrich official statistics for reporting on Sustainable Development Goals (SDG). However, survey data coming from an increased number of organizations (Small Data) and Big Data offer challenges [...] Read more.
As the world is digitizing fast, the increase in Big and Small Data offers opportunities to enrich official statistics for reporting on Sustainable Development Goals (SDG). However, survey data coming from an increased number of organizations (Small Data) and Big Data offer challenges in terms of data heterogeneity. This paper describes a methodology for combining various data sources to create a more comprehensive dataset on SDG 6.1.1. (proportion of population using safely managed drinking water services). We enabled digital volunteers to trace buildings on satellite imagery and used the traces on OpenStreetMap to facilitate visual detection of water points on Unmanned Aerial Vehicle (UAV) imagery and estimate the number of people served per water point. Combining data on water points identified on our UAV imagery with data on water points from field surveys improves the overall quality in terms of removal of inconsistencies and enrichment of attribute information. Satellite imagery enables scaling more easily than UAV imagery but is too costly to acquire at sufficiently high resolution. For small areas, our workflow is cost-effective in creating an up-to-date and consistent water point dataset by combining UAV imagery, Volunteered Geographic Information, and field survey data. Full article
(This article belongs to the Special Issue Measuring and Monitoring Progress towards SDGs by Integrating Geospatial and Statistical Information)
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