RESEARCH AND PRACTICE ON SPATIO-TEMPORAL BIG DATA CLOUD PLATFORM OF THE BELT AND ROAD INITIATIVE

Spatio-temporal big data cloud platform is an important spatial information infrastructure that can provide different period spatial information data services, various spatial analysis services and flexible API services. Activities of policy coordination, facilities connectivity and unimpeded trade on the Belt and Road Initiative (B&R) will create huge demands to the spatial information infrastructure. This paper focuses on researching a distributed spatio-temporal big data engine and an extendable cloud platform framework suits for the B&R and some key technologies to implement them. A distributed spatio-temporal big data engine based on Cassandra™ and an extendable 4-tier architecture cloud platform framework is put forward according to the spirit of parallel computing and cloud service. Four key technologies are discussed: 1) a storage and indexing method for distributed spatio-temporal big data, 2) an automatically collecting, processing, mapping and updating method of authoritative spatio-temporal data for web mapping service, 3) a schema of services aggregation based on nodes registering and services invoking based on view extension, 4) a distributed deployment and extension method of the cloud platform. We developed a distributed spatio-temporal big data centersoftware and founded the main node platform portal with MapWorld™ map services and some thematic information services inChina and built some local platform portals for those countries in the B&R area. The management and analysis services for spatio-temporal big data were built in flexible styles on this platform. Practices show that we provide a flexible and efficient solution tobuild the distributed spatio-temporal big data center and cloud platform, more node portals can be aggregated to the main portal bypublishing their own web services and registering them in the aggregation schema. The data center and platform can support thestorage and management of massive data well and has higher fault tolerance and better scalability

Impact Of The “Belt And Road” Initiative On The Development Level Of E-commerce In 18 Provinces And Cities Along China ——Based On PSM-DID Method

E-commerce can effectively overcome market obstacles and directly connect consumers with enterprises, and has contributed greatly to the construction of China ’s “Belt and Road”. At the same time, the construction of the “Belt and Road” has also provided new opportunities for the sustained and healthy growth of e-commerce. In order to explore the specific impact of the “Belt and Road” initiative on the level of e-commerce development in 18 provinces and cities along China\u27s borders, panel data from 31 provinces in China from 2012 to 2017 were used to measure the 31 provinces in 6 years using the vertical and horizontal gap method. The level of e-commerce development, and then using the propensity score matching double difference method (PSM-DID) to explore the impact of the “Belt and Road” initiative on the level of e-commerce development in 18 provinces and cities along China and 13 provinces and cities along non-routes. The results show that the level of e-commerce development in China\u27s provinces or regions is not balanced. Guangdong, Shandong, Jiangsu, and Zhejiang have good e-commerce development levels, and Tibet, Ningxia, Qinghai, and Hainan have poor e-commerce development levels. Eastern regions The level of e-commerce development is higher and the growth rate is faster, while the level of e-commerce development in the western region is lower and the growth rate is slower; the Belt and Road initiative can significantly promote the level of e-commerce development in 18 provinces and cities along China, with a promotion effect of 1.71%. And it can promote the vigorous development of e-commerce by increasing regional GDP, increasing mobile phone users and Internet users, and increasing per capita disposable income in cities and towns

Atmospheric and ecosystem big data providing key contributions in reaching United Nations' sustainable development goals

Big open data comprising comprehensive, long-term atmospheric and ecosystem in-situ observations will give us tools to meet global grand challenges and to contribute towards sustainable development. United Nations' Sustainable Development Goals (UN SDGs) provide framework for the process. We present synthesis on how Station for Measuring Earth Surface-Atmosphere Relations (SMEAR) observation network can contribute to UN SDGs. We describe SMEAR II flagship station in Hyytiala, Finland. With more than 1200 variables measured in an integrated manner, we can understand interactions and feedbacks between biosphere and atmosphere. This contributes towards understanding impacts of climate change to natural ecosystems and feedbacks from ecosystems to climate. The benefits of SMEAR concept are highlighted through outreach project in Eastern Lapland utilizing SMEAR I observations from Varrio research station. In contrast to boreal environment, SMEAR concept was also deployed in Beijing. We underline the benefits of comprehensive observations to gain novel insights into complex interactions between densely populated urban environment and atmosphere. Such observations enable work towards solving air quality problems and improve the quality of life inside megacities. The network of comprehensive stations with various measurements will enable science-based decision making and support sustainable development by providing long-term view on spatio-temporal trends on atmospheric composition and ecosystem parameters.Peer reviewe

Consumer Data Research

Big Data collected by customer-facing organisations – such as smartphone logs, store loyalty card transactions, smart travel tickets, social media posts, or smart energy meter readings – account for most of the data collected about citizens today. As a result, they are transforming the practice of social science. Consumer Big Data are distinct from conventional social science data not only in their volume, variety and velocity, but also in terms of their provenance and fitness for ever more research purposes. The contributors to this book, all from the Consumer Data Research Centre, provide a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors – and a timely appraisal of the ways in which consumer data challenge scientific orthodoxies

Consumer Data Research

Big Data collected by customer-facing organisations – such as smartphone logs, store loyalty card transactions, smart travel tickets, social media posts, or smart energy meter readings – account for most of the data collected about citizens today. As a result, they are transforming the practice of social science. Consumer Big Data are distinct from conventional social science data not only in their volume, variety and velocity, but also in terms of their provenance and fitness for ever more research purposes. The contributors to this book, all from the Consumer Data Research Centre, provide a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors – and a timely appraisal of the ways in which consumer data challenge scientific orthodoxies

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

Consumer Data Research

Big Data collected by customer-facing organisations – such as smartphone logs, store loyalty card transactions, smart travel tickets, social media posts, or smart energy meter readings – account for most of the data collected about citizens today. As a result, they are transforming the practice of social science. Consumer Big Data are distinct from conventional social science data not only in their volume, variety and velocity, but also in terms of their provenance and fitness for ever more research purposes. The contributors to this book, all from the Consumer Data Research Centre, provide a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors – and a timely appraisal of the ways in which consumer data challenge scientific orthodoxies

Belt & Road Initiative in Times of ‘Synchronized Downturn’

Nearly ten years since the official launch of the Belt and Road Initiative (BRI), an understanding of what the initiative’s objectives are consolidated. However, the short-, mid-, and long-term implications of the initiative are less clear. This is reflected in academic research, as well as in policy-oriented publications stemming from the global think-tank sector. This collection adds to this debate by offering a glimpse into selected aspects of BRI and its development, including the applicability of existing theories of trade to the case of BRI, the specificity of investment modes associated with BRI, sustainability, SDGs, socio-cultural issues, and many other implications. Due to its focus on diverse aspects of BRI, this collection will be of interest to students of international economics, international relations, and related subjects

Modelling past and future land use changes and potential conflicts from mining, agriculture, and industry in the rapidly developing region of Kuantan, Malaysia

Kuantan is emerging as a dynamically developing region supported by the megaeconomic development projects such as the East Coast Economic Development Plan in conjunction with the extension of China-Malaysia bilateral industrial parks and establishment of East Coast Rail Link (ECRL), a part of the great Belt and Road Initiative (BRI). Such a rapidly developing region requires a robust spatial analysis to understand the changing landscape pattern and its socio-environmental impacts to guide sustainable development. Addressing the lack of research focused on this key economic development region, this study aims to characterise and evaluate the historic and future projection of land use land cover (LULC) change patterns to understand the dynamics of the regional development process and to identify potential future land use conflicts. The methodology for this research includes construction of coarse-scale land cover classes by using Landsat 5 TM and Landsat 8 OLI data based on a combination of Random Forest classifier on Google Earth Engine (GEE) platform and manual refinement to construct fine-scale LULC maps by using auxiliary reference data. The produced timeseries imageries’ overall accuracy assessment scored at an average of 83%. Subsequently, to further assess and model the future LULC change pattern, the Land Change Modeler (LCM) in TerrSet was utilized by training the multilayer perceptron (MLP) neural network and using the Markov chain analysis. The study shows that the region’s land cover will be largely altered by human intervention driven by urbanisation and the region’s evolving economic vision. Overall, the LULC timeseries for the years 2010 to 2020 revealed a prominent increase in oil palm plantation, followed by mining, residential, and industrial site expansion, with a consequent decline in forest and disturbed vegetation cover. The future land use projection for the year 2030 also revealed similar land use development patterns. Both the historical remote sensing data and future projections showed that industry, mining, and residential are clustered and growing in close proximity while expanding extensively, which may likely be a cause of future land use conflict. Although modelled future projections may contain many uncertainties, having the ability to envision future possible scenarios provide key insights into the current and evolving future patterns of land use changes and predicting their impacts on people and the environment. This will assist government bodies, stakeholders, and policy makers by providing information essential for future planning and sustainable development decisions