Potential of Using Night-Time Light to Proxy Social Indicators for Sustainable Development

Andries, Ana; Morse, Stephen; Murphy, Richard J.; Sadhukhan, Jhuma; Martínez-Hernández, Elías; Amezcua‐Allieri, Myriam A.; Aburto, Jorge

doi:10.3390/rs15051209

Cited by 6 publications

(3 citation statements)

References 172 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For traffic land, although we lack direct traffic flow data on each road, the 130-m night-light data is used. This resolution is much better than commonly used data, such as the DMSP and the VIIRS, which have resolutions of 1 km and 500 m, respectively (Andries et al 2023;Zhang et al 2021). Because most vehicles turn their lights on during night travel, this method is feasible, as confirmed by other studies (Andreano et al 2021;Kabanda 2020).…”

Section: Discussionmentioning

confidence: 60%

Promoting low-carbon land use: from theory to practical application through exploring new methods

Chuai,

Xu,

Liu

et al. 2024

Humanit Soc Sci Commun

View full text Add to dashboard Cite

Cities are main carbon emissions generators. Land use changes can not only affect terrestrial ecosystems carbon, but also anthropogenic carbon emissions. However, carbon monitoring at a spatial level is still coarse, and low-carbon land use encounters the challenge of being unable to adjust at the patch scale. This study addresses these limitations by using land-use data and various auxiliary data to explore new methods. The approach involves developing a high-resolution carbon monitoring model and investigating a patch-scale low-carbon land use model by integrating high carbon sink/source images with the Future Land Use Simulation model. Between 2000 and 2020, the results reveal an increasing trend in both carbon emissions and carbon sinks in the Shangyu district. Carbon sinks can only offset approximately 3% of the total carbon emissions. Spatially, the north exhibits net carbon emissions, while the southern region functions more as a carbon sink. A total of 14.5% of the total land area witnessed a change in land-use type, with the transfer-out of cropland constituting the largest area at 96.44 km2, accounting for 50% of the total transferred area. Land-use transfer resulted in an annual increase of 77.72 × 104 t in carbon emissions between 2000 and 2020. Through land-use structure optimisation, carbon emissions are projected to increase by only 7154 t C/year from 2000 to 2030, significantly lower than the amount between 2000 and 2020. Further low-carbon land optimisation at the patch scale can enhance the carbon sink by 129.59 t C/year. The conclusion drawn is that there is considerable potential to reduce carbon emissions through land use control. The new methods developed in our study can effectively contribute to high-resolution carbon monitoring in spatial contexts and support low-carbon land use, promoting the application of low-carbon land use from theory to practice. This will provide technological guidance for land use planning, city planning, and so forth.

show abstract

Section: Discussionmentioning

confidence: 60%

Promoting low-carbon land use: from theory to practical application through exploring new methods

Chuai,

Xu,

Liu

et al. 2024

Humanit Soc Sci Commun

View full text Add to dashboard Cite

show abstract

“…The UN has acknowledged the importance and role that EO data can play in many aspects of the effort to complete the SDGs, either in measuring and monitoring environmental phenomena or in serving as a backdrop for analysis and research. Of particular interest are the effort to use EO in national statistics for monitoring and measuring indicators relevant to agriculture and land management (UN et al 2017), the use of EO for monitoring SDG indicators and identifying social gaps at more local scales (Andries et al 2023), and the use of EO to face challenges in local sustainability (Moallemi et al 2020), thus supporting the effort to leave no one behind.…”

Section: Geographic Information and Earth Observation Imagerymentioning

confidence: 99%

Volunteered Geographic Information, Citizen Science and Machine Learning in the Service of Sustainable Development Goals and the Sendai Framework

Antoniou

2023

Citizen Science: Theory and Practice

View full text Add to dashboard Cite

This paper focuses on two prominent efforts tackling global problems, namely the UN Sustainable Development Goals (SDGs) and the Sendai Framework (SF). To achieve the aims sought by these initiatives or to observe and measure their effectiveness and progress, accurate and up-to-date information is needed. An important part of this information refers to geographic information (GI). GI is the fundamental underpinning element that spans the globe, captures time, and functions as the common denominator of many variables and data from other domains. Herein, several enabling factors related to GI are highlighted, and their intertwining impact is examined relative to the aims of SDGs and SF. These factors are Earth observation (EO) imagery enhanced with the advances in machine learning (ML), citizen science (CS), and volunteered geographic information (VGI). The synergy of these factors can be used to bring, on the one hand, the high-level policies and discourse from a theoretical level down to more practical implementations, and on the other hand, enable individual and localized efforts to scale up easily in both developed and developing countries and produce the desired results.

show abstract

“…Poverty is a global social problem, and poverty eradication is the first of the 17 sustainable development goals proposed by the United Nations [1][2][3][4][5]. The issue of poverty is particularly prominent in China.…”

Section: Introductionmentioning

confidence: 99%

Grid-Scale Poverty Assessment by Integrating High-Resolution Nighttime Light and Spatial Big Data—A Case Study in the Pearl River Delta

Li,

Lin,

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Poverty is a social issue of global concern. Although socioeconomic indicators can easily reflect poverty status, the coarse statistical scales and poor timeliness have limited their applications. While spatial big data with reasonable timeliness, easy access, and wide coverage can overcome such limitations, the integration of high-resolution nighttime light and spatial big data for assessing relative poverty is still limited. More importantly, few studies have provided poverty assessment results at a grid scale. Therefore, this study takes the Pearl River Delta, where there is a large disparity between the rich and the poor, as an example. We integrated Luojia 1-01, points of interest, and housing prices to construct a big data poverty index (BDPI). To evaluate the performance of the BDPI, we compared this new index with the traditional multidimensional poverty index (MPI), which builds upon socioeconomic indicators. The results show that the impoverished counties identified by the BDPI are highly similar to those identified by the MPI. In addition, both the BDPI and MPI gradually decrease from the center to the fringe of the study area. These two methods indicate that impoverished counties were mainly distributed in ZhaoQing, JiangMen and HuiZhou Cities, while there were also several impoverished parts in rapidly developing cities, such as CongHua and HuaDu Counties in GuangZhou City. The difference between the two poverty assessment results suggests that the MPI can effectively reveal the poverty status in old urban areas with convenient but obsolete infrastructures, whereas the BDPI is suitable for emerging-development areas that are rapidly developing but still lagging behind. Although BDPI and MPI share similar calculation procedures, there are substantial differences in the meaning and suitability of the methodology. Therefore, in areas lacking accurate socioeconomic statistics, the BDPI can effectively replace the MPI to achieve timely and fine-scale poverty assessment. Our proposed method could provide a reliable reference for formulating targeted poverty-alleviation policies.

show abstract

Potential of Using Night-Time Light to Proxy Social Indicators for Sustainable Development

Cited by 6 publications

References 172 publications

Promoting low-carbon land use: from theory to practical application through exploring new methods

Promoting low-carbon land use: from theory to practical application through exploring new methods

Volunteered Geographic Information, Citizen Science and Machine Learning in the Service of Sustainable Development Goals and the Sendai Framework

Grid-Scale Poverty Assessment by Integrating High-Resolution Nighttime Light and Spatial Big Data—A Case Study in the Pearl River Delta

Contact Info

Product

Resources

About