ESTIMATING INDUSTRIAL STRUCTURE CHANGES IN CHINA USING DMSP – OLS NIGHT-TIME LIGHT DATA DURING 1999–2012

The Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) night-time light imagery has been proved to be a powerful tool to monitor economic development with its relatively high spatial resolution at large scales. Night-time lights caused by human activities derived from DMSP-OLS satellite imagery are widely used in socioeconomic parameter estimations and urbanization monitoring. In this paper, DMSP-OLS night-time stable light data from 1999 to 2012 are utilized to analyze inter-annual variation in GDP of per unit light intensity (RGDP) in China. Furthermore, RGDP was compared with statistical data of the tertiary industry structure for 28 provincial regions. The results show that the provincial RGDP decreased abruptly in 2001–2002, 2008–2009 and 2011–2012, which is consistent with the proportional growth of the tertiary industry in GDP. These results indicate that the changes in RGDP can reflect tertiary industry structural changes in China's province-level regions

Spatiotemporal Variations of City-Level Carbon Emissions in China during 2000–2017 Using Nighttime Light Data

China is one of the largest carbon emitting countries in the world. Numerous strategies have been considered by the Chinese government to mitigate carbon emissions in recent years. Accurate and timely estimation of spatiotemporal variations of city-level carbon emissions is of vital importance for planning of low-carbon strategies. For an assessment of the spatiotemporal variations of city-level carbon emissions in China during the periods 2000–2017, we used nighttime light data as a proxy from two sources: Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data and the Suomi National Polar-orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The results show that cities with low carbon emissions are located in the western and central parts of China. In contrast, cities with high carbon emissions are mainly located in the Beijing-Tianjin-Hebei region (BTH) and Yangtze River Delta (YRD). Half of the cities of China have been making eorts to reduce carbon emissions since 2012, and regional disparities among cities are steadily decreasing. Two clusters of high-emission cities located in the BTH and YRD followed two dierent paths of carbon emissions owing to the diverse political status and pillar industries. We conclude that carbon emissions in China have undergone a transformation to decline, but a very slow balancing between the spatial pattern of high-emission versus low-emission regions in China can be presumed

Utility of High Resolution Human Settlement Data for Assessment of Electricity Usage Patterns

Electricity is vital for modern human civilization, and its demands are expected to significantly rise due to urban growth, transportation modernization, and increasing industrialization and energy accessibility. Meeting the present and future demands while minimizing the environmental degradation from electricity generation pathways presents a significant sustainability challenge. Urban areas consume around 75% of global energy supply yet urban energy statistics are scarce all over the world, creating a severe hindrance for the much-needed energy sustainability studies. This work explores the scope of geospatial data-driven analysis and modeling to address this challenge. Identification and measurements of human habitats, a key measure, is severely misconceived. A multi-scale analysis of high, medium, and coarse resolution datasets in Egypt and Taiwan illustrates the increasing discrepancies from global to local scales. Analysis of urban morphology revealed that high-resolution datasets could perform much better at all scales in diverse geographies while the power of other datasets rapidly diminishes from the urban core to peripheries. A functional inventory of urban settlements was developed for three cities in the developing world using very high-resolution images and texture analysis. Analysis of correspondence between nighttime lights emission, a proxy of electricity consumption, and the settlement inventory was the conducted. The results highlight the statistically significant relationship between functional settlement types and corresponding light emission, and underline the potential of remote sensing data-driven methods in urban energy usage assessment. Lastly, the lack of urban electricity data was addressed by a geospatial modeling approach in the United States. The estimated urban electricity consumption was externally validated and subsequently used to quantify the effects of urbanization on electricity consumption. The results indicate a 23% lowering of electricity consumption corresponding to a 100% increase in urban population. The results highlight the potential of urbanization in lowering per-capita energy usage. The opportunity and limits to such energy efficiency were identified with regards to urban population density. The findings from this work validate the applicability of geospatial data in urban energy studies and provide unique insights into the relationship between urbanization and electricity demands. The insights from this work could be useful for other sustainability studies

Nighttime Lights as a Proxy for Economic Performance of Regions

Studying and managing regional economic development in the current globalization era demands prompt, reliable, and comparable estimates for a region’s economic performance. Night-time lights (NTL) emitted from residential areas, entertainment places, industrial facilities, etc., and captured by satellites have become an increasingly recognized proxy for on-ground human activities. Compared to traditional indicators supplied by statistical offices, NTLs may have several advantages. First, NTL data are available all over the world, providing researchers and official bodies with the opportunity to obtain estimates even for regions with extremely poor reporting practices. Second, in contrast to non-standardized traditional reporting procedures, the unified NTL data remove the problem of inter-regional comparability. Finally, NTL data are currently globally available on a daily basis, which makes it possible to obtain these estimates promptly. In this book, we provide the reader with the contributions demonstrating the potential and efficiency of using NTL data as a proxy for the performance of regions

Spatial heterogeneity of human activities and its driving factors in karst areas of Southwest China over the past 20 years

Guizhou Province is located in the karst mountain regions of Southwest China, where the ecological environment is extremely fragile and particularly sensitive to human activities. Therefore, understanding the changing characteristics and driving factors of human activity in recent decades is urgent. In this study, least squares, correlation analysis, spatial autocorrelation analysis, and GeoDetector model were used. Based on a large collection of nightlight, topography, and meteorological data, combined with geographical information technology, we investigated the spatial distribution, intensity change, and degree of impact of human activity from 2000 to 2020. The results showed that during the study period, human activities intensity and range were expanding. The human activity areas expanded from 15,963 to 86,923 km2 at an increasing rate of 4,279.2 km2/a, which was 1,118.4 km2/a from 2000 to 2010 and 6,375.3 km2/a from 2010 to 2020. The human activity intensity increased from 0.009 to 0.018, with an increasing rate of 0.0006/a, which was 0.00028/a from 2000 to 2010 and 0.00073/a from 2010 to 2020. The rate of increase in the human activity intensity and the rate of expansion in the human activity range from 2010–2020 were significantly higher than that from 2000–2010, becoming more notable (p ≤ 0.01), especially after 2012. With the expansion of human activities, the center of gravity of human activity moved towards the northeast by 20.71 km. The human activities were primarily distributed in areas with the gentlest slopes (6–15°), middle and low altitudes (489–1,982 m), suitable temperatures (12.36–17.74°C), and abundant precipitation (1,001.99–1,276.99 mm). The research results using the GeoDetector model indicate that slope had the greatest impact on human activities with a q value of 0.1338; precipitation, elevation, and temperature had q values of 0.0626, 0.0253, and 0.0136, respectively. The combined impact between the precipitation and slope was the greatest with a q value of 0.1803. In Guizhou Province, under policy guidance, human activities that promoted vegetation change accounted for 79.60%. This study attempts to enhance sustainable development and provides valuable information on the environmental protection of karst mountain regions

Computational socioeconomics

Uncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development. The understanding of socioeconomic processes provides foundations to quantify global economic development, to map regional industrial structure, and to infer individual socioeconomic status. In this review, we will make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools, data-driven methods, theoretical models and novel applications at multiple resolutions, including the quantification of global economic inequality and complexity, the map of regional industrial structure and urban perception, the estimation of individual socioeconomic status and demographic, and the real-time monitoring of emergent events. This review, together with pioneering works we have highlighted, will draw increasing interdisciplinary attentions and induce a methodological shift in future socioeconomic studies

Multi-scale and dynamic energy mapping for strategic decision making and integrated energy management in Wallonia

In the context of smart cities, this doctoral thesis addresses the energy challenge linked to existing building stocks, by proposing methods and tools for estimating and analysing their energy consumption on the territorial scale, in combination with multi-scale and dynamic energy mapping. The methodologies and tools developed are applied to the entire stock of buildings in Wallonia (Belgium), which includes more than 1.7 million buildings. The results should help implement smart energy management in large building stocks. Firstly, the annual heat consumption (HC), heat demand (HD), and electricity consumption (EC) of the regional building stock are assessed, statistically analysed and mapped on different scales. Based on mean values at the neighbourhood scale, the HD is lower than the HC of 16.44%, 15.78% and 9.26% for the residential, tertiary industrial buildings respectively. Statistical analysis tests were performed to analyse to what extent different types of variables explain the annual EC. Moreover, the impact of climate change on the existing building stock's HC and cooling EC evolution until 2050 is performed using artificial intelligence models. The HC reduction of the entire building stock until 2050, calculated at the regional scale, reaches -8.82 % for residential, -10.00% for tertiary, and -11.26% for industrial buildings. The projected increase in EC for cooling in existing tertiary buildings is + 11.94% in 2050. Further, the land use mix (LUM) of residential, tertiary and industrial buildings on a statistical sector scale is assessed based on entropy (E) and Herfindahl-Hirschman Index (HHI). On the 12 generated LUM classes, 3 prospective scenarios based on climate change, buildings renovation rate, and demography are applied. Energy consumption reduction tendencies are different in classes. Finally, the dynamic hourly HC and EC profiles per m² of different building archetypes are modelled, using sigmoid functions and programming in Python, based on previously assessed annual HC and EC and the temperature data. The simulated dynamic hourly profiles of HC and EC of 4 building archetypes are calibrated and validated using monitoring data and indices proposed by ASHRAE.Wal-e-Citie

Book of short Abstracts of the 11th International Symposium on Digital Earth

The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium

Geo-Information Technology and Its Applications

Geo-information technology has been playing an ever more important role in environmental monitoring, land resource quantification and mapping, geo-disaster damage and risk assessment, urban planning and smart city development. This book focuses on the fundamental and applied research in these domains, aiming to promote exchanges and communications, share the research outcomes of scientists worldwide and to put these achievements better social use. This Special Issue collects fourteen high-quality research papers and is expected to provide a useful reference and technical support for graduate students, scientists, civil engineers and experts of governments to valorize scientific research