The Symbolization of Regional Elements Based on Local-Chronicle Text Mining and Image-Feature Extraction
Abstract
:1. Introduction
2. Case Study and Data Source
3. Methods
3.1. A Method for Extracting Semantic Relations of Regional Cultural Elements
3.1.1. Text Pre-Processing
3.1.2. Text Information Mining Based on Jieba Word Separation Package
3.1.3. Constructing Text Co-Occurrence Networks Based on Gephi Software
- Average Degree: this is the average value of the degree (number of connections to other nodes) of all nodes in the network. It can be used to reflect the average connectivity of nodes in the network.
- Graph density is defined as the ratio of the number of edges present in the network to the maximum number of edges that could be present. It is used as a measure of the network’s connectivity.
- Average clustering coefficient: the average of the clustering coefficients of individual nodes in the network, which reflects the local clustering characteristics of the network and indicates the tendency of keywords to cluster in the network.
- Modularity: an indicator of the quality of the community structure in the network. Modular community detection allows the identification of keyword clusters that may correspond to different topics or concepts in the text.
3.2. Symbol Design Methodology for Regional Cultural Elements
3.2.1. Segmenting the Main Body of the Image Based on GrabCut Algorithm
3.2.2. Extracting Image Subject Contour Based on Canny Algorithm
3.2.3. Extraction of Image Theme Colors Based on K-Means Clustering Algorithm
3.2.4. Symbol Simplification Based on Cartographic Generalization Ideas
3.2.5. Symbol Evaluation Based on Questionnaire Survey
4. Results
- The textual information was primarily categorized into three thematic contents: monuments and sites, historical buildings, and literary and artistic carvings.
- The regional elements characterized by distinct local features predominantly included the Shaolin Temple, Ancient Pagoda, Zhongyue Temple, and so on.
- The regional elements with strong relevance included the Shaolin Temple and monks, the Shaolin Temple and monasteries, sites and cultural relic protection, and so on.
5. Discussion
6. Conclusions
- In terms of regional-element extraction, this study took local chronicles as the research object, utilized text mining technology to extract keywords in local chronicle texts through Jieba, and then applied Gephi software to construct a word co-occurrence network. This effectively revealed the correlation and mutual influence between different feature elements, allowing for the accurate identification of the regional elements in the study area.
- In terms of map symbol design, this study adopted a series of image-processing techniques. The GrabCcut algorithm was used to effectively segment the main body of the features, the Canny edge-detection algorithm was used to accurately identify the edges and shapes of the features, and the K-means clustering algorithm was used to extract the color schemes. Finally, Adobe Illustrator (2020) software was used to combine the symbols and simplify their shapes, creating map symbols with regional characteristics that were easy to identify. This effectively improved the information transfer efficiency and visual aesthetics of the map.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References
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Type | Content | Source | Data Advantage |
---|---|---|---|
Text | Monuments and heritage section | Dengfeng City Chronicle in 2008 | Official Detailed Logical |
Image | Folklore and cultural elements, landmarks, etc. Shaolin Temple Astronomical Observation Terrace Buddhist monks | Dengfeng City Government Official Website (www.dengfeng.gov.cn/) Baidu Gallery (image.baidu.com/) | Authoritative Rich variety High-resolution |
Number | Word | Frequency | Part of Speech |
---|---|---|---|
1 | Shaolin Temple | 147 | Noun—place name |
2 | Architecture | 125 | Noun |
3 | Sculpture | 94 | Verb |
4 | Historical remains | 86 | Noun |
5 | Cultural relic | 74 | Noun |
6 | Basilica | 74 | Noun |
7 | Monks | 69 | Noun |
8 | Zhongyue Temple | 68 | Noun—place name |
9 | Inscription | 64 | Noun |
10 | Songyang Academy | 63 | Noun—place name |
11 | Monastery | 60 | Noun |
12 | Existing | 47 | Time word |
13 | The republican period | 45 | Time word |
14 | Ancient pagoda | 40 | Noun |
Word | Shaolin Temple | Historical Remains | Architecture | Cultural Relic | Sculpture | Inscription | Monastery | Monk | Songyang Academy |
---|---|---|---|---|---|---|---|---|---|
Shaolin Temple | 0 | 2 | 1 | 7 | 0 | 17 | 13 | 33 | 6 |
Historical remains | 0 | 0 | 2 | 4 | 0 | 11 | 0 | 0 | 13 |
Architecture | 14 | 1 | 0 | 1 | 5 | 1 | 12 | 1 | 3 |
Cultural relic | 12 | 19 | 2 | 0 | 3 | 8 | 0 | 1 | 6 |
Sculpture | 8 | 3 | 4 | 1 | 0 | 7 | 0 | 5 | 2 |
Inscription | 1 | 0 | 3 | 0 | 0 | 0 | 5 | 7 | 8 |
Monastery | 2 | 0 | 0 | 0 | 0 | 6 | 0 | 3 | 2 |
Monk | 13 | 0 | 2 | 0 | 1 | 0 | 12 | 0 | 3 |
Songyang Academy | 5 | 1 | 0 | 0 | 0 | 7 | 0 | 0 | 0 |
Method | Principle | Typical Example |
---|---|---|
Delete | To better display and emphasize key features of a map object, it is occasionally necessary to exaggerate fragmented areas that may be overlooked at conventional scales of demarcation. This approach serves to highlight their significance or unique characteristics within the overall context. | |
Stretch | By reducing the number of bends in the line, the outline becomes smooth and clear, eliminating unnecessary complexity and preserving the more intuitive shape characteristics. | |
Merge | To simplify the overall map structure, adjacent or functionally similar contours should be combined into a larger unit. This approach not only reduces the over-representation of detail, but also enhances map readability by emphasizing key features. |
ID | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 | ID | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 5 | 5 | 4 | 4 | 4 | 4 | 5 | 26 | 4 | 4 | 4 | 5 | 4 | 4 | 4 |
2 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 27 | 4 | 4 | 4 | 4 | 4 | 5 | 5 |
3 | 5 | 4 | 3 | 5 | 4 | 5 | 5 | 28 | 5 | 3 | 5 | 5 | 4 | 4 | 5 |
4 | 4 | 5 | 4 | 5 | 4 | 5 | 4 | 29 | 5 | 5 | 4 | 3 | 5 | 4 | 5 |
5 | 5 | 4 | 5 | 4 | 4 | 5 | 4 | 30 | 4 | 4 | 5 | 4 | 5 | 5 | 4 |
6 | 4 | 4 | 4 | 5 | 3 | 5 | 4 | 31 | 4 | 5 | 4 | 4 | 5 | 4 | 4 |
7 | 5 | 4 | 4 | 4 | 5 | 4 | 4 | 32 | 4 | 5 | 5 | 5 | 5 | 4 | 4 |
8 | 4 | 4 | 5 | 4 | 4 | 5 | 4 | 33 | 4 | 5 | 5 | 4 | 4 | 4 | 5 |
9 | 5 | 4 | 5 | 4 | 4 | 4 | 5 | 34 | 4 | 4 | 4 | 4 | 4 | 5 | 5 |
10 | 5 | 4 | 5 | 5 | 5 | 5 | 4 | 35 | 5 | 4 | 4 | 5 | 4 | 5 | 5 |
11 | 4 | 5 | 4 | 5 | 4 | 4 | 4 | 36 | 3 | 4 | 3 | 3 | 2 | 3 | 3 |
12 | 3 | 4 | 3 | 4 | 2 | 4 | 4 | 37 | 5 | 4 | 4 | 5 | 5 | 5 | 4 |
13 | 5 | 4 | 4 | 5 | 4 | 5 | 4 | 38 | 5 | 5 | 5 | 4 | 4 | 5 | 4 |
14 | 5 | 4 | 5 | 5 | 5 | 4 | 5 | 39 | 5 | 4 | 5 | 4 | 5 | 4 | 5 |
15 | 5 | 5 | 4 | 4 | 4 | 5 | 4 | 40 | 4 | 5 | 5 | 5 | 4 | 5 | 5 |
16 | 4 | 4 | 5 | 5 | 4 | 5 | 4 | 41 | 4 | 3 | 4 | 2 | 3 | 4 | 3 |
17 | 5 | 3 | 4 | 5 | 4 | 4 | 5 | 42 | 3 | 4 | 4 | 3 | 4 | 3 | 3 |
18 | 4 | 5 | 4 | 4 | 4 | 5 | 4 | 43 | 2 | 4 | 2 | 4 | 3 | 2 | 4 |
19 | 4 | 5 | 5 | 4 | 5 | 4 | 4 | 44 | 3 | 4 | 3 | 4 | 2 | 4 | 4 |
20 | 5 | 4 | 5 | 5 | 5 | 4 | 5 | 45 | 4 | 5 | 5 | 5 | 5 | 5 | 4 |
21 | 4 | 4 | 5 | 5 | 5 | 5 | 4 | 46 | 4 | 2 | 4 | 4 | 4 | 4 | 2 |
22 | 5 | 4 | 4 | 5 | 5 | 3 | 5 | 47 | 3 | 3 | 4 | 3 | 4 | 2 | 3 |
23 | 5 | 5 | 4 | 5 | 4 | 5 | 5 | 48 | 5 | 4 | 3 | 3 | 4 | 2 | 4 |
24 | 4 | 5 | 4 | 5 | 4 | 4 | 4 | 49 | 4 | 2 | 3 | 3 | 4 | 2 | 3 |
25 | 4 | 4 | 5 | 4 | 5 | 5 | 4 | 50 | 5 | 4 | 3 | 4 | 2 | 3 | 5 |
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Share and Cite
Wu, L.; Cao, D.; Yang, J.; Zhang, R.; Yan, X. The Symbolization of Regional Elements Based on Local-Chronicle Text Mining and Image-Feature Extraction. ISPRS Int. J. Geo-Inf. 2024, 13, 299. https://doi.org/10.3390/ijgi13090299
Wu L, Cao D, Yang J, Zhang R, Yan X. The Symbolization of Regional Elements Based on Local-Chronicle Text Mining and Image-Feature Extraction. ISPRS International Journal of Geo-Information. 2024; 13(9):299. https://doi.org/10.3390/ijgi13090299
Chicago/Turabian StyleWu, Lili, Di Cao, Jinjin Yang, Ruoyi Zhang, and Xinran Yan. 2024. "The Symbolization of Regional Elements Based on Local-Chronicle Text Mining and Image-Feature Extraction" ISPRS International Journal of Geo-Information 13, no. 9: 299. https://doi.org/10.3390/ijgi13090299
APA StyleWu, L., Cao, D., Yang, J., Zhang, R., & Yan, X. (2024). The Symbolization of Regional Elements Based on Local-Chronicle Text Mining and Image-Feature Extraction. ISPRS International Journal of Geo-Information, 13(9), 299. https://doi.org/10.3390/ijgi13090299