Using multi-source big data to understand the factors affecting urban park use in Wuhan

2019 ◽  
Vol 43 ◽  
pp. 126367 ◽  
Author(s):  
Feinan Lyu ◽  
Li Zhang
Keyword(s):  
Big Data ◽  
Urban Park ◽  
Factors Affecting ◽  
Park Use

A geographical detector study on factors influencing urban park use in Nanjing, China

2021 ◽  
Vol 59 ◽  
pp. 126996
Author(s):  
Zhengxi Fan ◽  
Jin Duan ◽  
Yin Lu ◽  
Wenting Zou ◽  
Wenlong Lan
Keyword(s):  
Urban Park ◽  
Park Use ◽  
Factors Influencing ◽  
Geographical Detector

scholarly journals Applying Improved Apriori Algorithm in Figuring out the Relation between Weather Factors and Rainfall

2020 ◽  
Vol 1 (1) ◽  
pp. 23-26
Author(s):  
Siti Zulaikha ◽  
Martaleli Bettiza ◽  
Nola Ritha
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Light Intensity ◽  
Apriori Algorithm ◽  
Weather Factors ◽  
Factors Affecting ◽  
Data Store ◽  
Repeated Pattern ◽  
Major Factors ◽  
Sun Light

Data on the rainfall is compelling to study as it becomes one of the major factors affecting the weather in a certain region and various aspects of life as well. Generally, predicting rainfall is performed by analyzing data in the past in certain methods. Rainfall is prone to follow repeated pattern in sequence of time. The utilization of big data mining is expected to result in any valuable information that used to be unrevealed in the big data store. Some methods used in data mining are Apriori Algorithm and Improved Apriori Algorithm. Improved Apriori itself is to represent the database in the form of matrix to describe its relation in the database. Data used in this research is the rainfall factor in 2016 in Tanjungpinang city. Based on the test of Improved Apriori Algorithm, it was found out that the relation of the rainfall and weather factors utilizing 2 item sets, that is, if the temperature is low (24,0 - 26,0), the humidity is high (85 - 100), then the rainfall is mild. If the temperature is low (24,0 - 26,0), the light intensity is low (0 – 3), then the rainfall is heavy, and 3 item sets if the temperature is low (24,0 - 26,0), the humidity is high (85 - 100), the sun light intensity is low (0-3), then the rainfall is medium.

scholarly journals Establishment of variation source and age-related reference interval models for 22 common biochemical analytes in older people using real-world big data mining

2020 ◽  
Vol 49 (6) ◽  
pp. 1062-1070
Author(s):  
Chaochao Ma ◽  
Liangyu Xia ◽  
Xinqi Chen ◽  
Jie Wu ◽  
Yicong Yin ◽  
...  
Keyword(s):  
Big Data ◽  
Older People ◽  
Real World ◽  
Reference Interval ◽  
Reference Intervals ◽  
Ageing Population ◽  
Factors Affecting ◽  
Age Related ◽  
The Status ◽  
Source Models

Abstract Background the ageing population has increased in many countries, including China. However, reference intervals (RIs) for older people are rarely established because of difficulties in selecting reference individuals. Here, we aimed to analyse the factors affecting biochemical analytes and establish RI and age-related RI models for biochemical analytes through mining real-world big data. Methods data for 97,220 individuals downloaded from electronic health records were included. Three derived databases were established. The first database included 97,220 individuals and was used to build age-related RI models after identifying outliers by the Tukey method. The second database consisted of older people and was used to establish variation source models and RIs for biochemical analytes. Differences between older and younger people were compared using the third database. Results sex was the main source of variation of biochemical analytes for older people in the variation source models. The distributions of creatinine and uric acid were significantly different in the RIs of biochemical analytes for older people established according to sex. Age-related RI models for biochemical analytes that were most affected by age were built and visualized, revealing various patterns of changes from the younger to older people. Conclusion the study analysed the factors affecting biochemical analytes in older people. Moreover, RI and age-related RI models of biochemical analytes for older people were established to provide important insight into biological processes and to assist clinical use of various biochemical analytes to monitor the status of various diseases for older people.

scholarly journals Understanding an Urban Park through Big Data

2019 ◽  
Vol 16 (20) ◽  
pp. 3816 ◽  
Author(s):  
Jisoo Sim ◽  
Patrick Miller
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Planning Process ◽  
Big Data Analytics ◽  
Urban Park ◽  
Social Media Analytics ◽  
Forest Park ◽  
Planning And Design ◽  
Data Source

To meet the needs of park users, planners and designers must know what park users want to do and how they want the park to offer different activities. Big data may help planners and designers gain this knowledge. This study examines how big data collected in an urban park could be used to identify meaningful implications for planning and design. While big data have emerged as a new data source, big data have not become an accepted source of data due to a lack of understanding of big data analytics. By comparing a survey as a traditional data source with big data, this study identifies the strengths and weaknesses of using big data analytics in park planning and design. There are two research questions: (1) what activities do park users want; and (2) how satisfied are users with different activities. The Gyeongui Line Forest Park, which was built on an abandoned railway, was selected as the study site. A total of 177 responses were collected through the onsite survey, and 3703 tweets mentioning the park were collected from Twitter. Results from the survey show that ordinary activities such as walking and taking a rest in the park were the most common. These findings also support existing studies. The results from social media analytics found notable things such as positive tweets about how the railway was turned into a park, and negative tweets about diseases that may occur in the park. Therefore, a survey as traditional data and social media analytics as big data can be complementary methods for the design and planning process.

scholarly journals Risk factors for Keshan disease: a prospective cohort study protocol of gut flora

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Zhenzhen Li ◽  
Jin Wei ◽  
Yanping Zhang ◽  
Gaopeng Li ◽  
Huange Zhu ◽  
...  
Keyword(s):  
Big Data ◽  
Cohort Study ◽  
Rapid Development ◽  
Keshan Disease ◽  
Molecular Pathways ◽  
Gut Flora ◽  
Factors Affecting ◽  
New Biomarkers ◽  
The Relationship

Abstract Background Keshan disease is an endemic cardiomyopathy of undefined causes. Being involved in the unclear pathogenesis of Keshan disease, a clear diagnosis, and effective treatment cannot be initiated. However, the rapid development of gut flora in cardiovascular disease combined with omics and big data platforms may promote the discovery of new diagnostic markers and provide new therapeutic options. This study aims to identify biomarkers for the early diagnosis and further explore new therapeutic targets for Keshan disease. Methods This cohort study consists of two parts. Though the first part includes 300 participants, however, recruiting will be continued for the eligible participants. After rigorous screening, the blood samples, stools, electrocardiograms, and ultrasonic cardiogram data would be collected from participants to elucidate the relationship between gut flora and host. The second part includes a prospective follow-up study for every 6 months within 2 years. Finally, deep mining of big data and rapid machine learning will be employed to analyze the baseline data, experimental data, and clinical data to seek out the new biomarkers to predict the pathogenesis of Keshan disease. Discussion Our study will clarify the distribution of gut flora in patients with Keshan disease and the abundance and population changes of gut flora in different stages of the disease. Through the big data platform analyze the relationship between environmental factors, clinical factors, and gut flora, the main factors affecting the occurrence of Keshan disease were identified, and the changed molecular pathways of gut flora were predicted. Finally, the specific gut flora and molecular pathways affecting Keshan disease were identified by metagenomics combined with metabonomic analysis. Trial registration: ChiCTR1900026639. Registered on 16 October 2019.

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