Analysis of Green Spaces by Utilizing Big Data to Support Smart Cities and Environment: A Case Study About the City Center of Shanghai

Abstract

Green areas or parks are the best way to encourage people to take part in physical exercise. Traditional techniques of researching the attractiveness of green parks, such as surveys and questionnaires, are naturally time consuming and expensive, with less transferable outcomes and only site-specific findings. This research provides a factfinding study by means of location-based social network (LBSN) data to gather spatial and temporal patterns of green park visits in the city center of Shanghai, China. During the period from July 2014 to June 2017, we examined the spatiotemporal behavior of visitors in 71 green parks in Shanghai. We conducted an empirical investigation through kernel density estimation (KDE) and relative difference methods on the effects of green spaces on public behavior in Shanghai, and our main categories of findings are as follows: (i) check-in distribution of visitors in different green spaces, (ii) users’ transition based on the hours of a day, (iii) famous parks in the study area based upon the number of check-ins, and (iv) gender difference among green park visitors. Furthermore, the purpose of obtaining these outcomes can be utilized in urban planning of a smart city for green environment according to the preferences of visitors.

Big Geospatial Data or Geospatial Big Data? A Systematic Narrative Review on the Use of Spatial Data Infrastructures for Big Geospatial Sensing Data in Public Health

“…Geography (general, remote sensing, geoscience) [12][13][14][15][16][17][18][19][20][21] 10 Public health [22][23][24][25][26][27][28][29][30][31] 10 Environment (physical, built environment) [32][33][34][35][36][37][38][39] 8 Science (computer, engineering, multidisciplinary) [40][41][42][43] 4…”

“…China [13][14][15][23][24][25][26][27][28][29][30][33][34][35][36][37] 16 India [41] 1 Indonesia [31] 1…”

“…Environments Livability [15], green space [13,14], night lights [19], noise exposure [39,40], land use [16], park visits [14], water points [12], indoor/outdoor air pollutants [17,20,23,24,29,33,36,39], energy expenditure [39], NDVI [38], mountain green cover [32], low-elevation coastal zones [28], anthropogenic heat emissions [34], socioeconomic factors [29] 23…”

Directions of Development of Adaptive Systems to the Operating Conditions of Mobile Wood Chopping Machines with Low Power Engines

“…An increase in awareness of threats related to violating the standards for emission of harmful substances into the air, especially in large cities, enforces radical actions from politicians and local authorities. The directions of such works include reducing harmful anthropogenic emissions in cities [1], and natural cleansing of these areas by developing the infrastructure of green areas in the city [2,3]. Trees in such areas absorb exhaust fumes, dust and noise, and need to be cleared or trimmed [4].…”

Toward Near Real-Time Kinematics Differential Correction: In View of Geometrically Augmented Sensor Data for Mobile Microclimate Monitoring

“…Indeed, spatial enrichment of moving sensors leads directly to the possibility of having fast, reliable and accurate information over time. This, in turn, allows to set up added-value services for improving the life quality of the urban communities [2,3]. In this regard, estimation methods have been tested through mobile and fixed IoT sensors for a limited spatial resolution [4].…”