Big Data for Public Domain: A bibliometric and visualized study of the scientific discourse during 2000–2020

This article aims to investigate the trend of scientific publications under ‘big data and policy’ research during the last two decades, including the dynamics of the network structure of researchers and the institutions. Bibliometrics is utilized as a tool to reveal the dynamics of scientific discussions that occur through articles, published in international journals indexed/contained in the Scopus database; meanwhile, the analysis visualization is performed by using VOSviewer 1.6.16. The search results indicate that the United States serves as the country of origin for most productive author affiliations in publishing articles, the University of Oxford (United Kingdom) serves as the home institution for most productive author affiliations, and Williamson, B., from the University of Edinburgh (United Kingdom), is considered as the most prolific writer. In addition, the Swiss Sustainability Journal from MDPI is cited as the source for the most widely discussed publication topic in its journals. Further, ‘Big Data for Development: A Review of Promises and Challenges’ is regarded as the article with the most references. Additionally, the most discussed topics on ‘big data and policy’ include smart cities, open data, privacy, artificial intelligence, machine learning, and data science

Methods and Data Sources for Measuring Socio-Economic Factors: A Literature Review

The compiling of the population data, to establish its socioeconomic factors, is a high-cost task for governments and regulatory organizations due to the need for financial and human resources. This limitation makes it almost impossible to count on immediate updated socioeconomic population information. This article compiles a series of alternative data sources and methods that can be applied to reduce the costs and the time required to update such information. The review focus on how these sources and methods have been used in developing countries during time, highlighting the solutions for satisfying the need of updated socioeconomic factors of the population

Using a hybrid methodology of dasyametric mapping and data interpolation techniques to undertake population data (dis)aggregation in South Africa

The ability of GIS to produce accurate analysis results is dependent on the accuracy and the resolution of the data. In many instances the resolution of census enumerator tract data is too coarse and therefore inefficient in conducting fine grained spatial analysis. Dasymetric techniques can increase the spatial resolution of data by incorporating related high resolution ancillary data layers allowing the primary data to be represented at finer resolutions. Areal interpolation relates to a geostatistical process of transferring data from one set of polygons to another. This paper proposes the application of a hybrid technique using dasymetric mapping and areal interpolation principals to overcome the issues of transferring data from arbitrary spatial units to fit for purpose analysis zones on demand. As a consequence the technique also overcomes the problems of coarse scale population data as well as issues relating to the modifiable areal unit problem (MAUP). The data used to illustrate the value and accuracy of the developed methodology is that of the 2011 census population data and ESKOM’s SPOT building count. The final outcome is an algorithm allowing the disaggregation and aggregation of population data to any spatial unit with a high level of accuracy.Keywords: Dasymetric Mapping; MAUP; Population; Census; GI

QUANTIFYING THE RELATIONSHIP BETWEEN NATURAL AND SOCIOECONOMIC FACTORS AND WITH FINE PARTICULATE MATTER (PM2.5) POLLUTION BY INTEGRATING REMOTE SENSING AND GEOSPATIAL BIG DATA

PM2.5 pollution is an environmental issue results from various natural and socioeconomic factors, frequently witnessed in the spring and winter across mainland China. However, the dominant influence of natural and socioeconomic factors within a city on PM2.5 is not extensively studied yet. In this study, the Random Forest Regression (RFR) is utilized to quantify the relationships between PM2.5 and potential factors within Wuhan city on a typical day turn from winter to spring. Technically, the 24-hour average PM2.5 concentration in downtown area on February 17th 2017 are collected at 9 sites. In the meantime, we retrieve simultaneous aerosol depth optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS). The ground measured PM2.5 and AOD are coupled for the retrieval of near-surface PM2.5 concentration by Spatial-temporal CoKriging (STCK) with Normalized Vegetation Index (NDVI), Modified Normalized Water Index (MNDWI), Normalized Building Index (NDBI) from Landsat-8 and DEM from Shuttle Radar Topography Mission (SRTM). As the geospatial big data booms, the Internet-collected volunteered geographic information (VGI), representing the urban form and function, are integrating for the regression to obtain the spatial variables importance measures (VIMs) by RFR both in centre and sub-urban region of Wuhan. The results reveal that terrain characteristics and the density of industrial enterprises have obvious relationships with the accumulation of PM2.5 while the density of roads also contributes to this

A Study on Spatial and Temporal Aggregation Patterns of Urban Population in Wuhan City based on Baidu Heat Map and POI Data

Advanced technologies and big data have brought new visions and methods to urban planning research. Based on the Baidu heat map and POI data of two typical days (a weekend day and a workday) in 2018, this paper analyses the spatial and temporal aggregation patterns of crowds in the urban centre of Wuhan using ArcGIS. Aggregation patterns are defined by the intensity of population activities and the places where crowds gather. In terms of time, the daily change of population aggregation intensity is studied by counting the heat value of 24 moments captured throughout the day. The results show that on rest days, people prefer to travel around noon and in the afternoon, reaching the highest peak of the day around 15:00, while on workdays, residents\u27 activities are affected by commuting, with obvious \u27morning rush hours\u27 and \u27evening rush hours\u27. Firstly, the spatial correlation between the density of POI distribution and the degree of population aggregation has been studied by the spatial coupling relationship between the Baidu heat map and POI data. Secondly, the index of correlation between the aggregation of different POIs and population (ICPP) are mentioned to analyse the purposes and the degrees of aggregation during weekend and workday rush hours. Based on the ICPP, we analyse activities from three aspects: the different ICPPs between the workday and the weekend; the different ICPPs between the morning, afternoon and evening; and the different ICPPs among different POIs

SATELLITE AND ARTIFICIAL INTELLIGENCE IN MAPPING MULTIDIMENSIONAL POVERTY IN AFRICA

Context and background Multidimensional Poverty (MP) considers poverty in multiple dimensions of deprivations such as health, education, energy, the standard of living and access to basic services. MP remains a major challenge in Africa, with a large proportion of the population living in MP. According to United Nations Development Programme (UNDP), Africa has shown the highest Multidimensional Poverty Index (MPI) having over 40% of its population living in MP. Goal and Objectives: This paper is a review, aimed at assessing the potential of the integration of satellite and Artificial Intelligence (AI) in mapping MP, with a specific focus on Africa. Methodology: Based on the reviews of past studies, the combination of satellite data such as nighttime light, daytime satellite imagery and high-resolution settlement data in combination with techniques such as field surveys, statistical correlation models (transfer learning) and AI (deep learning) has been applied in mapping MP. Results: The findings from studies show that the combination of satellite data and AI has the capability of providing more accurate and granular MP maps, compared to the traditional approach. Again, this paper explains the concept of MP with a specific focus on Africa and presents a map depicting the current MPI in African countries. Finally, pitfalls especially in the accuracy, granularity and frequency of MP data were identified. Consequently, the satellite and AI approaches are recommended for more accurate, frequent, cost-effective and granular data, required in mapping poverty and design of interventions that effectively address the needs of the vulnerable populations in Africa.