Spatial relationship description model and algorithm of urban and rural planning in the smart city

Abstract

With the advancement of the concept of smart cities, research on urban and rural planning is also developing rapidly. How to make the spatial relationship description model in the context of smart cities more in line with the actual situation of urban development is still a problem to be solved. This article focuses on the spatial relationship description model and algorithm of urban and rural planning results in the context of smart cities, studies the impact of urban industrial structure under the action of the spatial relationship description model, and evaluates the impact of policy as well as the impact of smart city construction on urban green innovation efficiency. Its dynamic effects and urban heterogeneity are expanded and analyzed. The research results show that compared with the traditional ordinary least squares (OLS) technology, geographically weighted regression (GWR) has significantly improved the solution results of the model, the R value is reduced by 0.3–0.4, the residual sum of squares value is significantly reduced, and the Akaike information criterion value is significantly improved. For the same training samples and test data, compared with the OLS technology, the mean-squared error reduction rate of the prediction results obtained by the GWR technology basically reached more than 50%, especially in Experiment 4; it reached 57.9%, which shows a very obvious advantage in prediction accuracy. Smart city policies have a significant role in promoting green innovation efficiency in pilot areas, and over time, innovation efficiency has increased year by year, and the policy effects are particularly prominent in the eastern coastal areas with a good development foundation. Therefore, the construction of smart cities is based on the characteristics of the region, and it is necessary to seek a sustainable development path based on the conditions of the region.

Keywords

• smart city
• urban and rural planning results
• spatial relationship description model
• urban planning prediction model