Research On Spatial Agglomeration And Configured Driving Innovation System Of Urban Creative Industries

Creative industries are an important resource for national urban development.Spatial agglomeration of urban creative industries is the brand-new trend and direction of urban industrial construction,which has meaningful value in improving the quality of urban construction,strengthening the driving effects of industries,and realizing profound regional innovation.The cluster index correlation system is so complicated for it successfully draws from research results of economic geography and management engineering,highly integrates such innovative indicators as spatial economy,creative culture,agglomeration & diffusion,enterprise frameworks;and continuously strengthens the industrial management of urban regional spatial strategy.Scholars at home & abroad pay great attention to analyzing the characteristics of configured correlation under industrial spatial agglomeration,but it is also worth noting that the motivation of urban creative spatial agglomeration(the systematic construction between industrial spatial agglomeration,driving effects and impact mechanism)is relatively lacking in the field of urban creative industry spatial strategy.The spatial agglomeration innovation system shall be improved to provide the essential theoretical and empirical basis for vigorously developing the spatial supply of creative industries in China.Meanwhile,it can promote industrial configurations,build creative environment,guarantee the efficient operation of creative industries,and boost the comprehensive competitive strength of cities.This has important theoretical value and innovative significance,and is also the focus to further expand the creative industries’ functional configuration and spatial behavior of innovation.The target is to provide the driving innovation system of creative spatial agglomeration.Firstly,the index elements are extracted from the two levels of configured driving factors and impact mechanism respectively according to references,and the correlation hypothesis of spatial agglomeration is set.Secondly,an empirical test is carried out,and a multi-factor spatial clustering model is obtained through in-depth analysis of the strong & weak correlation between driving effects and impact mechanism.The DBICP algorithm is set up based on the model,which is to do the dynamic clustering simulation solution of Shanghai city.Finally,dynamic image output based on bayonet data and browser strategy is completed.There are six parts in total.Part One: Concepts & Overview(Chap.1 and Chap.2).It includes Introduction,Background,Progress Review,Logical Framework Discussion and Conceptual Boundaries Statement.It mainly discusses the evolution of spatial agglomeration of creative industries,spatial reconstruction of creative zones,spatial impact mechanism and the configuration driving effects.Through identifying the theoretical target and breakthrough,the research perspective,content,method and technical route are proposed,and the overall framework is constructed.Part Two: Model Index Extraction & Hypothesis(Chap.3).It consists of two parts: Firstly,the qualitative identification of spatial impact factors and driving effects of creative industries are studied based on the Grounded Theory.Using literature survey,scale interview,questionnaire survey,qualitative research and other methods,a large amount of data and interview scale are used to construct generic indicators such as basic elements of driving effects and impact mechanism,and then these indicators are refined.Statistics are translated with QSR NVi vo12 to establish spatial agglomeration factor index hypothesis,so 14 influence elements and 6 driving factors of creative industries are obtained.Then using AMOS to set up the structural equation to form the primary construction of driving effects and impact mechanism indicators.Secondly,the indexes are divided into information coupling,control coupling and control system coupling based on Coupling Theory.From the perspective of management engineering,the multifactor theoretical model of spatial agglomeration is constructed which provides hypothesis for scientific rationality of the research.Part Three: Model Validation & Modification(Chap.4).It consists of two parts: Firstly,the measurement comparison study is carried out by using the Herfindahl-Hirschman Index(HHI),employment number and output value location entropy according to objective materials,and then the strong correlation index of spatial agglomeration degree on impact mechanism is selected with STATA14.0 and TOPSIS algorithm.Secondly,the correlation between driving effects and impact mechanism index is verified with data panel model;Rationality and correctness of multi-factor theoretical model are proved,and this model is modified and improved.Part Four: Model Application & Solution(Chap.5).It consists of two parts: Firstly,the search model of BP-GA heuristic neural network is established with Matlab.According to overall optimization ability of the genetic algorithm,the clustering and combination data of several landmarks in Shanghai are collected to identify the structure and causal feedback relationship from the driving effects to spatial measure,and finally the satisfied region of spatial agglomeration is obtained.Secondly,using the correlation of agglomeration and impact mechanism,the Spatial Durbin Model(SDM)is used to detect the regional correlation in satisfied region.With the nested POI algorithm,the optimal clustering degree is iterated with strong migration ability,so as to create the DBICP subsystem flow chart and finally get the path of spatial dynamic clustering algorithm.Part Five: Model Simulation & Output(Chap.6).It consists of three parts: Firstly,after the background interactive translation of new algorithm code and Browser JS code,3D dynamic simulation is implemented with CANVAS container to further obtain the numerical and semantic driving mode of spatial clustering individuals and clusters.Secondly,BubbleSet and ARCGIS are overlapped by geographical view and bayonet space based on CANVAS assembled image.Thirdly,DBICP is visualized using E-CHARTS visual image to realize the simulation floating point conversion from mathematical expression of the first approximation model to the second,and finally to achieve dynamic visual display of spatial agglomeration of creative industries based on individual coordinates.Theoretical models become simple,visual,and intuitive.This can effectively predict the spatial dynamic clustering of urban creative industries according to the results of urban planning map overlap test.Part Six: Conclusions & Prospects(Chap.7).It includes the summarization,research feedbacks and prospects.The system dynamics model under the innovation-driven path of spatial agglomeration development of urban creative industries is given,which lays the foundation for future continuous scientific research.Conclusions: by constructing the multi-factor coupling index of spatial agglomeration,the composition relationship of spatial agglomeration of creative industries can be clearly reflected.The impact factors of spatial agglomeration are dynamically correlated with the internal & external driving effects.Internal effects have endogenous effects on impact factors,while external effects have extended effects on policy and market innovation.The spatial migration-agglomeration algorithm can effectively display the spatial agglomeration effects.E-CHARTS large data dynamic simulation model can show the main path of driving effects of spatial agglomeration,and identify the hot spots,paths and trends of the agglomeration.The innovations are as follows:(1)By literature review and interview survey,the index structure of driving effects and impact mechanism is studied,which reflects the index abundance of creative industries at spatial level.By the principle of regional economics,scientific model of management and mathematical analysis,the concept of spatial agglomeration of creative industries is explained.After building the logic framework of spatial agglomeration,according to the influencing factors,the Grounded Theory and Coupling Theory are applied to deconstruct and reconstruct the impact factors of spatial agglomeration,so as to obtain 14 factors and 6 indicators,which are then correlated with driving factors.Through interaction of innovation driving systems,endogenous driving and extensional driving,the complex systematic research on spatial agglomeration of urban creative industries is formed.The hypothesis of multi-factor theory model of spatial agglomeration of urban creative industries is obtained.It puts forward the related logic framework of driving effects,impact mechanism and spatial agglomeration,which has outstanding disciplinary prospective and theoretical innovation.(2)It is found that there is an indirect positive relationship between spatial agglomeration and configured driving system by measure analysis and empirical test.The interaction mechanism is tested with measure analysis and Topsis algorithm.The multi-factor theoretical model shows the dynamic relationship between the elements and driving effects of impact mechanism of urban creative industries.The driving effects have an effect on impact mechanism which can affect spatial agglomeration.Multiple linear regression indicates that there is an indirect relationship between the configured driving effects of urban creative industries and spatial agglomeration,which is also a typical spatial agglomeration path-dependent pattern.In the evolution process of spatial agglomeration,urban creative industries presents a "region-centered impact mechanism",which is in line with the functional description of the multi-functional interval of spatial agglomeration index and reflects the evolution rules.(3)The simulation experiment is completed with DBICP algorithm,and the spatial clustering of large data visual images under Browser strategies such as E-CHARTS is realized.This reflects the interdisciplinary,creative development and dynamic updating features between management engineering and urban planning,and enhances the applicability of visual analysis in urban regional development.With DBICP algorithm the algorithm code are interacted by JavaScript code.Based on the program coding,the weight information is assigned to impact factors and action mechanism,the spatial agglomeration effects of individuals in the initial pheromones of the creative industries are simulated which further improving the spatial simulation model.The background program is transplanted to the foreground to complete the E-CHARTS large data visualization dynamic simulation image,so as to test the practical application value of the theoretical model.This provides a basis for improving the construction of space agglomeration,which is of significant disciplinary pioneering and academic innovation.Research on spatial agglomeration and configured driving innovation is a complex system,which plays a guiding role in creative agglomeration area,creative space development and creative environmental policies,and also provides support for the management and control of urban engineering,the implementation of planning decisions and the practice of economic construction.The research is of great significance in promoting the innovative development of spatial configured driving behavior in creative industries.