A review of scientific advancements in datasets derived from big data for monitoring the Sustainable Development Goals

Xing

Journal of Advanced Transportation

et al. 2022

As the core element of social-economic development in the Qinghai-Tibet Plateau, transportation dramatically shapes the scale, type, and intensity of human activities. First, this study utilizes night light data and kilometer-grid population data to construct night light development index (NLDI) and to evaluate the human development level at the county scale. Then, based on the complex transportation infrastructure data, the weight assignment method is adopted to create transportation infrastructure influence degree (TIID), which is used to evaluate the location conditions of the counties. Finally, bivariate spatial autocorrelation is utilized to analyze the effect of regional conditions on the county-level human development variation. The results show that (1) NLDI is verified to assess differences in the level of human development among counties in Qinghai-Tibet Plateau and to overcome the difficulties of systematically and integrally obtaining socio-economic statistical data. The pattern of human development level in Qinghai-Tibet Plateau presents a “core-periphery” spatial structure with the transportation network as the axis. (2) On the whole, with the improvement of location conditions influenced by transportation infrastructure, the spatial aggregation of human development level is constantly improving, and the spatial disparity continues to decrease. (3) Locally, four spatial interaction patterns of high/low clustering are recognized and analyzed. It reflects the complexity and spatial heterogeneity between transport infrastructure construction and human development level in the Qinghai-Tibet Plateau.

Section: Night Light Development Index and Human Developmentmentioning

confidence: 99%

Two for the Road: Measuring Regional Disparity and Agglomeration in Human Development Level and Transportation Infrastructure

Xing

Journal of Advanced Transportation

et al. 2022

“…Facebook data is shown to be highly correlated with official data on digital gender gaps and their study is thus another important example showing how web data can expand coverage of development indicators (see also follow-up study Kashyap et al, 2020 ). In this line of research, several studies have discussed approaches to monitoring Sustainable Development Goals using big data (see for a review Allen et al, 2021 ).…”

Section: Big Data In Development Research: a Brief Overview And Its S...mentioning

confidence: 99%

Big data and development sociology: An overview and application on governance and accountability through digitalization in Tanzania

Schwitter

Pretari

Marwa³

et al. 2022

Front. Sociol.

The digital revolution and the widespread use of the internet have changed many realms of empirical social science research. In this paper, we discuss the use of big data in the context of development sociology and highlight its potential as a new source of data. We provide a brief overview of big data and development research, discuss different data types, and review example studies, before introducing our case study on active citizenship in Tanzania which expands on an Oxfam-led impact evaluation. The project aimed at improving community-driven governance and accountability through the use of digital technology. Twitter and other social media platforms were introduced to community animators as a tool to hold national and regional key stakeholders accountable. We retrieve the complete Twitter timelines up to October 2021 from all ~200 community animators and influencers involved in the project (over 1.5 million tweets). We find that animators have started to use Twitter as part of the project, but most have stopped tweeting in the long term. Employing a dynamic difference-in-differences design, we also do not find effects of Oxfam-led training workshops on different aspects of animators' tweeting behavior. While most animators have stopped using Twitter in the long run, a few have continued to use social media to raise local issues and to be part of conversations to this day. Our case study showcases how (big) social media data can be part of an intervention, and we end with recommendations on how to use digital data in development sociology.

“…Monitoring and managing environmental change is one of the key challenges for the 21 st century (Turner et al, 2007;Allen et al, 2021). Land cover change is both a key cause, and consequence, of environmental change (Lambin et al, 2001; Foley et al, 2005), and as such it is recognised as a key variable for characterising the environment (Rockström et al, 2009;Bojinski et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

LCM2021 – The UK Land Cover Map 2021

Marston

O’Neil

Morton

et al. 2023

Preprint

Abstract. Land cover is a key environmental variable, underpinning widespread environmental research and decision-making. The UK Centre for Ecology and Hydrology (UKCEH) have provided reliable land cover information since the early 1990’s; this supports multiple scientific, government and commercial objectives. Recent advances in computation and satellite data availability have enabled annual UKCEH land cover maps since 2017. Here we introduce the latest, annual UK Land Cover Map, representing 2021 (LCM2021) and describe its production and validation. LCM2021 methods replicate those for LCM2017 to LCM2020 with minor deviations to enhance accuracy. LCM2021 is based on the classification of satellite and spatial context data into 21 land cover/habitat classes, from which a product suite is derived. The production of LCM2021 involved three highly automated key stages: pre-processing of input data, image classification and production of the final data products. Google Earth Engine scripts were used to create an input data stack of satellite and context data. A set of training areas was created, based on data harvested from historic UKCEH land cover maps. The training data were used to construct a Random Forest classifier, which yielded classified images. Compiled results were validated against 35,182 reference samples, with correspondence tables indicating variable class accuracy and an overall accuracy of 82.6 % for the 21-class data and 86.5 % at a 10 aggregated-class level. The UK Land Cover Map product suite includes a set of raster products in various projections, thematic and spatial resolutions (10 m, 25 m and 1 km) and land-parcel/vector product. The data are provided in 21-class (all configurations) and aggregated 10-class versions (1 km raster products only). All raster products are freely available for academic and non-commercial research. The data for Great Britain (GB) are provided in the British National Grid projection (https://epsg.io/27700) and the Northern Ireland (NI) data are in the TM75 Irish National Grid (https://epsg.io/29903). Information on how to access the data is given in the Data Availability section of the paper.