scholarly journals Spatial Analysis of Urban Residential Sensitivity to Heatwave Events: Case Studies in Five Megacities in China

2021 ◽  
Vol 13 (20) ◽  
pp. 4086
Author(s):  
Guoqing Zhi ◽  
Bin Meng ◽  
Juan Wang ◽  
Siyu Chen ◽  
Bin Tian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Spatial Analysis ◽  
Urban Planning ◽  
Language Processing ◽  
Temporal Trends ◽  
New Paradigm ◽  
Geographic Space ◽  
Low Sensitivity ◽  
The Relationship

Urban heatwaves increase residential health risks. Identifying urban residential sensitivity to heatwave risks is an important prerequisite for mitigating the risks through urban planning practices. This research proposes a new paradigm for urban residential sensitivity to heatwave risks based on social media Big Data, and describes empirical research in five megacities in China, namely, Beijing, Nanjing, Wuhan, Xi’an and Guangzhou, which explores the application of this paradigm to real-world environments. Specifically, a method to identify urban residential sensitive to heatwave risks was developed by using natural language processing (NLP) technology. Then, based on remote sensing images and Weibo data, from the perspective of the relationship between people (group perception) and the ground (meteorological temperature), the relationship between high temperature and crowd sensitivity in geographic space was studied. Spatial patterns of the residential sensitivity to heatwaves over the study area were characterized at fine scales, using the information extracted from remote sensing information, spatial analysis, and time series analysis. The results showed that the observed residential sensitivity to urban heatwave events (HWEs), extracted from Weibo data (Chinese Twitter), best matched the temporal trends of HWEs in geographic space. At the same time, the spatial distribution of observed residential sensitivity to HWEs in the cities had similar characteristics, with low sensitivity in the urban center but higher sensitivity in the countryside. This research illustrates the benefits of applying multi-source Big Data and intelligent analysis technologies to the understand of impacts of heatwave events on residential life, and provide decision-making data for urban planning and management.

scholarly journals Study on the comprehensive computational thinking transformation of urban planning discipline in the era of big data

2020 ◽  
Author(s):  
Haixuan Zhu ◽  
◽  
Xiaoyu Jia ◽  
Pengluo Que ◽  
Xiaoyu Hou ◽  
...  
Keyword(s):  
Big Data ◽  
Urban Planning ◽  
Computer Science ◽  
Research Methods ◽  
Data Storage ◽  
Computational Thinking ◽  
Rural Planning ◽  
Human Beings ◽  
New Paradigm ◽  
Thinking Mode

In the era of big data, with the development of computer technology, especially the comprehensive popularization of mobile terminal device and the gradual construction of the Internet of Things, the urban physical environment and social environment have been comprehensively digitized and quantified. Computational thinking mode has gradually become a new thinking mode for human beings to recognize and govern urban complex system. Meanwhile computational urban science has become the main discipline development aspect of modern urban planning. Computational thinking is the thinking of computer science using algorithms based on time complexity and space complexity, which provides a new paradigm for the construction of index system, data collection, data storage, data analysis, pattern recognition, dynamic governance in the process of scientific planning and urban management. Based on this, this paper takes the computational thinking mode of urban planning discipline in big data era as the research object, takes the scientific construction of computational urban planning as the research purpose, and adopts literature research methods and interdisciplinary research methods, comprehensively studies the connotation of the computing thinking mode of computer science. Meanwhile, this paper systematically discusses the system construction of urban computing, model generation, the theory and method of digital twinning, as well as the popularization of the computational thinking mode of urban and rural planning discipline and the scientific research of computational urban planning, which responds to the needs of the era of the development of urban and rural planning disciplines in the era of big data.

scholarly journals The rise of big data and supporting technologies in keeping watch on the world's forests

2020 ◽  
Vol 22 (1) ◽  
pp. 129-141
Author(s):  
R. Taylor ◽  
C. Davis ◽  
J. Brandt ◽  
M. Parker ◽  
T. Stäuble ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Language Processing ◽  
Lower Cost ◽  
Forest Policy ◽  
Forest Products ◽  
Comprehensive Analysis ◽  
Geospatial Data ◽  
User Friendly ◽  
Analysis Of Social Networks

Technology-driven advances in the gathering, processing and delivery of big data are making it easier to monitor forests and make informed decisions over their use and management. This paper first describes how innovations in remote sensing and cloud computing are enabling generation of geospatial data more often, at lower cost and in more user-friendly formats. Second, it describes the evolution of systems and technologies to trace forest products, and agricultural commodities linked to deforestation, from source to final use. Third, it reviews the potential for emerging data mining technologies such as natural language processing, web scraping and computer vision to support forest policy analysis and augment geospatial data gathered through remote sensing. The paper gives examples of how these technologies are being used and may be used in the future to monitor and respond to deforestation, fire and natural disasters, improve governance by enabling faster and more comprehensive analysis of social networks, policies and regulations, and increase traceability and transparency within supply chains.

scholarly journals Personalized medicine into health national services: barriers and potentialities

2018 ◽  
Vol 33 (4) ◽  
pp. 159-163 ◽  
Author(s):  
Ana E. Rodríguez Vicente ◽  
Maria José Herrero Cervera ◽  
María Luisa Bernal ◽  
Luis Rojas ◽  
Ana M. Peiró
Keyword(s):  
Big Data ◽  
Clinical Practice ◽  
Personalized Medicine ◽  
New Paradigm ◽  
Policy Makers ◽  
Research And Innovation ◽  
Wide Range ◽  
The Relationship ◽  
Personalised Therapy ◽  
Guided Therapy

Abstract Research and innovation in personalized medicine (PM) are extensive and expanding, with several pharmacogenetic/pharmacogenomic (PGx) testing options currently available for a wide range of health problems. However, PGx-guided therapy faces many barriers to full integration into clinical practice and acceptance by practitioner/patient: utilization and uptake by payers in real-world practice are being discussed, and the criteria to guide clinicians and policy makers in PGx test selection are not fully incorporated. This review focuses on the advances of pharmacogenomics to individualize treatments, the relationship between pharmacogenetics and pharmacometabolomics, the new paradigm of the Big Data, the needs and barriers facing PGx clinical application and the situation of PGx testing in health national services. It is based on lectures presented by speakers of the European Society of Pharmacogenomics and Personalised Therapy (ESPT) Fourth Conference, held in Catania, October 4th, 2017.

A network model of happiness at destinations

2021 ◽  
Author(s):  
Arthur Huang
Keyword(s):  
Big Data ◽  
Language Processing ◽  
Driving Forces ◽  
Interdisciplinary Approach ◽  
Social Signals ◽  
Human Environment ◽  
Context Sensitive ◽  
Physical Conditions ◽  
Antecedents And Consequences ◽  
The Relationship

Understanding the antecedents and consequences of happiness at destinations is critical for building livable and sustainable communities for residents and tourists. Big data and social signals provide new opportunities to unpack the driving forces of happiness. For this study, geotagged social media data, physical environment data, and economic data are utilized to shed light on how neighborhood factors shape happiness. An interdisciplinary approach is adopted to integrate natural language processing, spatial analysis, network science, and statistical modeling. The results indicate that (1) crimes are negatively associated with neighborhood happiness; (2) visitor check-in activity mediates the relationship between places of interest and neighborhood happiness; (3) happy neighborhoods with similar happiness levels share higher numbers of common happy visitors, which implies that happy neighborhoods share attributes that attract happy visitors. This research contributes to theories regarding how neighborhood attributes may shape happiness, and demonstrates how big data can be used to characterize human-environment relationships for happiness-related research. Planners and tourism stakeholders can improve neighborhood happiness by engaging with residents and tourists to evaluate the current physical conditions of neighborhoods and develop context-sensitive plans and projects.

Geospatial Big Data: New Paradigm of Remote Sensing Applications

2019 ◽  
Vol 12 (10) ◽  
pp. 3841-3851 ◽  
Author(s):  
Xingdong Deng ◽  
Penghua Liu ◽  
Xiaoping Liu ◽  
Ruoyu Wang ◽  
Yuanying Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
New Paradigm ◽  
Sensing Applications ◽  
Remote Sensing Applications

Association Analysis of NDVI Changes and Topographic Factors

2013 ◽  
Vol 333-335 ◽  
pp. 1205-1208
Author(s):  
De Li Liu ◽  
Ya Shuang Zhang ◽  
Nan Lin
Keyword(s):  
Remote Sensing ◽  
Spatial Analysis ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Topographic Factors ◽  
Gis Software ◽  
Relationship Of ◽  
The Relationship ◽  
Slope Direction

Based on the TM remote sensing data of the Huadian city in 1991 and 2011 and based on the DEM data,using the normalized difference vegetation index (NDVI) change classification method,to Extraction the elevation,slope,slope direction data and the vegetation index data of the study area.Then using the spatial analysis function of GIS software to overlay the two different period NDVI data and analysis the NDVI change of area and spatial. Using the same method to overlay and analysis the relationship of NDVI data and elevation,slope,slope direction.Research shows that the variation of NDVI in the study area has relationship with the topographic factors change.

The Research on Implementation Methods of 3D GIS

2012 ◽  
Vol 170-173 ◽  
pp. 2840-2843
Author(s):  
Yan Li Gao ◽  
Wen Bin Li ◽  
Chang Zheng Shang
Keyword(s):  
Remote Sensing ◽  
Spatial Analysis ◽  
Urban Planning ◽  
Secondary Development ◽  
3D Gis ◽  
Remote Sensing Images

3D GIS is an intuitive and effective method of realistic geo-information. Its spatial analysis function meets the user’s needs of inquiring and analyzing to the geo-information. 3D GIS has been widely used in geoscience and urban planning. This article introduces the 3D GIS firstly. Then it discusses the implementation methods of 3D GIS in detail. These include underlying development, secondary development and implementation by remote sensing images. At last, their merits and faults are analyzed, and the paper gives the suitable conditions for different applications.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

ANALISIS KESESUAIAN LAHAN DAN EVALUASI JENIS TANAH DALAM BUDIDAYA TANAMAN TEBU UNTUK PENGEMBANGAN DAERAH KABUPATEN TEGAL

2013 ◽  
Vol 13 (2) ◽  
Author(s):  
Daru Mulyono
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Spatial Analysis ◽  
Geographical Information System ◽  
Plant Productivity ◽  
Saccharum Officinarum ◽  
Geographical Information ◽  
Sugarcane Plant ◽  
Plant Cultivation ◽  
Suitability Map

The objectives of the research were to make land suitability map for sugarcane plant (Saccharum officinarum), to give recommendation of location including area for sugarcane plant cultivation and to increase sugarcane plant productivity. The research used maps overlay and Geographical Information System (GIS) which used Arch-View Spatial Analysis version 2,0 A in Remote Sensing Laboratory, Agency for the Assessment and Application of Technology (BPPT), Jakarta. The research was carried out in Tegal Regency starting from June to October 2004.The results of the research showed that the suitable, conditionally suitable, and not suitable land for sugarcane cultivation in Tegal Regency reached to a high of 20,227 ha, 144 ha, and 81,599 ha respectively. There were six most dominant kind of soil: alluvial (32,735 ha), grumosol 5,760 ha), mediteran (17,067 ha), latosol   (18,595 ha), glei humus (596 ha), and regosol (22,721 ha).

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