Research On Decision Making For Building Public Service Infrastructure In Smart City

Smart city can be briefly explained as those cities that utilise information and communication technologies with the aim to increase the life quality of their inhabitants while providing sustainable development. Through implementing information and communication technologies into municipal services, cities turn into being more intelligent in their management of resources.Currently, the construction and development of smart city has many problems, both practically and theoretically, such as lack of regional specialties, resource integration difficulties, inappropriate technology for services, etc. Therefore, this thesis aims at studying the spatial planning of urban public service infrastructure by utilizing big data, and the design of smart services based on social demands. To achieve this goal, this thesis develops a decision support platform to storage and integrate those massive data information. This platform also collects multiple analysis tools or mathmetical models to do spatial analysis and data mining. It also benefits for the sharing and reuse of decision resources and knowledge. In the end, it can promote some scientific and effective decision suggests. The main research contents and methods of this thesis is as follows:Firstly, this thesis builds an open decision support platform by using big data, cloud computing, web services and other technologies, including database, model library, knowledge base, etc. The database of this platform mainly contains the spatial data of urban infrastructure and population, their attribute data, survey data for social demands, and the semantic text data. The decision model library involves abundant spatial anlysis tools from ArcMap software as well as numerous data mining methods. The decision support platform contributes to the application of "smart city" connotations and intelligent technologies into building urban public service infrastructure.Secondly, by utilizing intelligent technologies, the Spatial Decision Support System (SDSS) can obtain timely, accurate, comprehensive spatial data and attribute data for both population and facilities. With those location big data, this thesis calculates the density of population to analyze its spatial distribution. It also evaluates the spatial and time accessibility of public service infrastructure, taking their spatial distribution and social demands into consideration. Finaly, it provides optimized decision plans to improve the equity and timeliness of public resources' allocation.Thirdly, on the basis of extensive investigation, this thesis excavates the characteristics and preferences of residents' social demand, by using various data mining methods and models, including statistical analysis, structural equation model (SEM) and social network analysis (SNA). Under these circumstances, it provides decision-making guidance for the application of intelligent public services. It can improve the quality of public service as well as the public's overall satisfaction, to better realize the functions of smart city.To summerize, the main conclusion of this thesis are as follows:(1) built an Spatial Decision Support System (SDSS) for public service facilities based on GIS, and developed a cloud decision support platform based on data mining of social demands; (2) analyzed the accessibility evaluation and optimization decision-making of public service infrastructure in Hexi, Nanjing, through location big data and spatial analysis tool provided by ArcMap; (3) provided decision guidance for the implementation of intelligent public services based on the analysis of the characteristics and preferences of social demands. With these conclusions, the public service infrastructure in smart cities can bring the public more equitable, timely, suitable and satisfactory urban services.