Qi ZHOU, Changchun GAO. The Calculation and Visual Optimization Method of Spatial Dynamic Agglomeration Evolution of Urban Creative Industries [J]. Journal of Geo-information Science, 2020, 22(5): 1033
- Journal of Geo-information Science
- Vol. 22, Issue 5, 1033 (2020)
Fig. 1. Visual flow chart of dynamic clustering DBICP image in urban creative industry zone
Fig. 2. Visual flow chart of dynamic clustering DBICP image in urban creative industry zone
Fig. 3. Extraction results of DBSCAN algorithms
Fig. 4. Spatial Clustering and Merging Strategy of DBICP Algorithms
Fig. 5. Comparison of clustering 1effect before and after improvement of DBICP algorithm（Putuo District, Shanghai）
Fig. 6. DBICP algorithm clustering optimization effect
Fig. 7. DBICP algorithm Threshold color value planning optimization
Fig. 8. DBICP Optimal path demonstration images
Fig. 9. Pattern flow chart of ant colony algorithm
Fig. 10. Average path length and best path length effect diagram
Fig. 11. A preliminary linear trajectory of creative industry spatial agglomeration based on DBICP algorithm
Fig. 12. Image visualization based on browser strategy
Fig. 13. E-charts spatial aggregation dynamic interface search results in Putuo District, Shanghai
Fig. 14. E-charts spatial agglomeration dynamic global interface search results in Pudong District, Shanghai
Fig. 15. E-charts spatial aggregation dynamic global interface search results in Xujiahui District, Shanghai
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Table 1. Index set of key influencing factors based on spatial dynamic agglomeration of Urban Creative Industry Zone
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Table 2. Attribute classification of traffic flow gate types
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Table 3. Data selection of traffic flow
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Table 4. Preliminary geographic information of POI BUBBE-SET spatial aggregation （Putuo District）
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Table 5. Comparisons and evaluation based on several algorithms
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