The spatio-temporal big data platform is the infrastructure of city, which supports the aggregation, sharing and coordination of various types of information resources for smart city to provide the integration and sharing of urban public information resources. The land space information platform is a unified work of natural resources to support the convergence, sharing and coordination of information resources related to natural resources. Both are essential platforms for data resource aggregation and sharing.
The "DIKW" model of the spatio-temporal big data platform
The core of the spatio-temporal big data platform is to aggregate, integrate and manage various types of information resources in the city based on a unified spatio-temporal framework. Through open platforms, it provides shared services such as spatio-temporal information, spatial big data mining analysis, and visualization for all areas of smart city applications. The process can be implemented according to the "DIKW" model based on "four libraries" of entity library, indicator library, model library and knowledge library.
The entity library stores the original data, such as population, land, house, etc. The indicator library stores information, which is the statistical data obtained by the aggregated data, such as per capita living space and daily average passenger flow. The model library stores knowledge, which operates the combined calculation through the construction of analytical models, such as using the resource carrying capacity model to analyze urban resource carrying capacity, using the land development intensity model to analyze urban land development intensity. The knowledge library stores “wisdom", which can reflect the intelligent attributes through the prediction of future. For instance, model analysis can be used to predict the passenger flow in planning stage.
Data integration based on entity
According to the concept of entity, the various data resources of the city are processed and designed into a unified entity code. Through coding, the relationship between the entity and the business thematic data is established, and the multi-source data based on the entity can be integrated. In the application process, entities and associated data, functions are encapsulated into APIs (application programming interfaces) to serve different fields of application.
Dynamic monitoring based on indicator
The construction of the indicator system can be sorted out based on the ideas of systemization, specialization and normalization. In the systematization, by establishing the index library and the dimension library, the original report database can be changed into index database to express muti-dimensional data from different dimensions.
All indicators can be calculated in real time based on the entity library. They are periodic and quantifiable, which is conducive to monitor continuously.
Scientific monitoring based on model
Model is a method of feature description and expression with certain rules. In the field of smart cities, the urban service capacity can be analyzed through the public service capacity and ecological livability. In the field of natural resources, the area carrying capacity can be mined through the evaluation of resource and environment.
Focusing on the problems that need to be solved, selecting entity data and indicator information flexibly, building models to mine the relationship between elements, phenomena and features, and analyzing the causes of problems are the important supports of scientific monitoring.
Decision optimization based on knowledge
Knowledge is a combination of scientific calculations and expert experience to predict and optimize what might happen in the future. In the field of smart cities, through the dynamic monitoring and forecasting information of water conditions, rain conditions and so on, the probability and time period of intrinsic occurrence, simulation of the impact range and development trend of the city can be analyzed. It can provide early warning and supports for flood control and emergency plans. In the field of natural resources, through population and economic development potential forecasting, the total regional land use and development potential can be analyzed.
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