Spatial Planning Data Structure Based on Blockchain Technology
Abstract
:1. Introduction
2. Methods
2.1. Block Structure
2.1.1. Block Structure of Structured Data
- (1)
- Block Body of Structured Data
- (2)
- Block Header of Structured Data
- (3)
- Primary Unit Division for Structured Data
2.1.2. Block Structure of Unstructured Data
- (1)
- Block Body of Unstructured Data
- (2)
- Block Header of Unstructured Data
2.2. Chain Structure
2.2.1. Initializing the Chain Structure of Spatial Planning Data
2.2.2. Updating the Chain Structure of Spatial Planning Data
2.3. Consensus Algorithms
3. Experiments
3.1. Data
- (1)
- Spatial Planning Data
- (2)
- Current Spatial Data
- (3)
- Other Data
3.2. Experimental Procedures
3.2.1. Primary Unit Division for Structured Data
- (1)
- Defining Regional Classes and Extracting Minimum Elements
- (2)
- Combining Minimum Elements
- (3)
- Dividing, Calibrating, and Optimizing Primary Units
3.2.2. Spatial Planning Data Structures
- (1)
- Generating Primary Unit Data
- (2)
- Generating Block Structures
- (3)
- Verifying Block Structures
The Pseudocode for Consensus Verification of Primary Unit Data in Blocks | |
1 | //Pseudocode for Hyperledger Fabric 2.0 Chaincode, compliant with Java 8 Standard |
2 | @Transaction(intent = Transaction.TYPE.SUBMIT) |
3 | public Response validatePrimaryUnitData(Context context, String type, String key, String value) { |
4 | try { |
5 | //Parse the input data into a list of data entries |
6 | List<Map<String, Object>> inputDataList = parseInputData(value); |
7 | //Iterate over each data entry in the data list |
8 | for (Map<String, Object> dataEntry: inputDataList) { |
9 | String dataType = dataEntry.get(“dataType”).toString(); |
10 | //Validate based on data type |
11 | Response result; |
12 | if (“structured”.equals(dataType)) { |
13 | result = validateStructuredData(dataEntry); |
14 | } else if (“unstructured”.equals(dataType)) { |
15 | result = validateUnstructuredData(dataEntry); |
16 | } else { |
17 | //Unknown data type case |
18 | return new Response(Response.Status.FAILURE, “Unknown data type.”); |
19 | } |
20 | //If any validation fails, return a failure response |
21 | if (result.getStatus() == Response.Status.FAILURE) { |
22 | return result; |
23 | } |
24 | } |
25 | //All validations passed |
26 | return new Response(Response.Status.SUCCESS, “All data entries are valid.”); |
27 | } catch (Exception e) { |
28 | //Handle exceptions generically |
29 | return new Response(Response.Status.ERROR, “Validation error.”); |
30 | } |
31 | } |
32 | //Method to validate structured data |
33 | private Response validateStructuredData(Map<String, Object> dataEntry) { |
34 | //Perform quality, spatial, and indicator validations for structured data |
35 | boolean valid = ConsensusValidator.validateQuality(dataEntry).SUCCESS |
36 | ConsensusValidator.validateSpatial(dataEntry).SUCCESS |
37 | ConsensusValidator.validateIndicator(dataEntry).SUCCESS; |
38 | return valid ? new Response(Response.Status.SUCCESS, “Structured data valid.”) |
39 | : new Response(Response.Status.FAILURE, “Structured data validation failed.”); |
40 | } |
41 | //Method to validate unstructured data |
42 | private Response validateUnstructuredData(Map<String, Object> dataEntry) { |
43 | //Perform MD5 validation for unstructured data |
44 | boolean valid = ConsensusValidator.validateMD5(dataEntry).SUCCESS; |
45 | return valid ? new Response(Response.Status.SUCCESS, “Unstructured data valid.”) |
46 | : new Response(Response.Status.FAILURE, “Unstructured data validation failed.”); |
47 | } |
48 | //Example input data structure for validation |
49 | String value = “{ \”id\”: 202407270001, \”data\”: [ { \”dataType\”: \”structured\”, \”floorAreaRatio\”: 2.0, \”planningLandBoundaryPoint\”: \”MULTIPOLYGON(((lat1 lon1, lat2 lon2, …)))\”, … }, { \”dataType\”: \”unstructured\”, \”fileMD5\”: \”D56D71ECADF2137BE09D8B1D35C6C042\”, …} ] }”; |
- (4)
- Storing Block Structures on the Chain
3.3. Results Evaluation
3.3.1. Evaluating the Rationality of Primary Unit Division
3.3.2. Efficiency Assessment of Spatial Planning Data Structure
- (1)
- Block Size and Data Redundancy Evaluation
- (2)
- Consensus Algorithm Efficiency and Data Throughput Evaluation
3.3.3. Comprehensive Assessment of Results
4. Discussion
4.1. The Applications in Monitoring and Early Warning
4.1.1. Spatial Monitoring and Early Warning
- (1)
- Inputting Elements:
- (2)
- Calculating Intersections:
- (3)
- Calculating and Outputting Results:
4.1.2. Indicator Monitoring and Early Warning
- (1)
- Inputting Elements
- (2)
- Analyzing Trends
- (3)
- Calculating and Outputting Results
4.2. Generalizability, Limitations, and Future Research Considerations
- (1)
- Enhancing Cross-Industry Coordination and Expanding Users Using Cross-Chain Technology
- (2)
- Providing Public Disclosure and Verification Services Using Off-Chain Interaction Technology
5. Conclusions
6. Patents
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Field Name | Field Code | Field Type | Field Length |
---|---|---|---|
Land use code | YDCode | Varchar | 50 |
Land use type | YDType | Varchar | 10 |
Land use name | YDName | Varchar | 50 |
Regulatory detailed planning code | GHCode | Numeric | 38 |
Regulatory detailed planning name | GHName | Varchar | 100 |
Floor area ratio | Capability | Numeric | 38 |
Building height limit | BLDHLimit | Numeric | 38 |
Building density | BLDDensity | Numeric | 38 |
Greening rate | GreenRate | Numeric | 38 |
Total area | AreaTotal | Numeric | 38 |
Land area | AreaYD | Numeric | 38 |
Road area | AreaRoad | Numeric | 38 |
Green area | AreaGreen | Numeric | 38 |
Building area | AreaBuild | Numeric | 38 |
Population size | PopNum | Numeric | 38 |
Population density | PopDensity | Numeric | 38 |
Supporting service facilities | PubEstab | Varchar | 200 |
Planning land boundary point | Shape | Binary | Indefinite Length |
Dimension | Dimension Weight | Specific Indicators | Indicator Weight |
---|---|---|---|
Education facilities (A) | 0.138 | Kindergarten coverage (A1) | 0.393 |
Primary school coverage (A2) | 0.321 | ||
Middle school coverage (A3) | 0.286 | ||
Government services (B) | 0.115 | Community public service center coverage (B1) | 0.354 |
Subdistrict administrative management coverage (B2) | 0.314 | ||
Police station coverage (B3) | 0.332 | ||
Public culture (C) | 0.241 | Community cultural activity room coverage (C1) | 0.186 |
Cultural activity center coverage (C2) | 0.152 | ||
Regional level or above cultural venues coverage (C3) | 0.127 | ||
Street green space coverage (C4) | 0.191 | ||
Community park coverage (C5) | 0.180 | ||
Comprehensive park coverage (C6) | 0.164 | ||
Sports facilities (D) | 0.122 | Community multi-function sports field coverage (D1) | 0.393 |
National fitness activity center coverage (D2) | 0.324 | ||
Regional level or above sports facilities coverage (D3) | 0.283 | ||
Medical health (E) | 0.132 | Community health service station coverage (E1) | 0.376 |
Subdistrict health service center coverage (E2) | 0.321 | ||
Hospital coverage (E3) | 0.303 | ||
Welfare care (F) | 0.115 | Home care service station coverage (F1) | 0.529 |
Day care center coverage (F2) | 0.471 | ||
Daily life (G) | 0.137 | Nursing home coverage (G1) | 0.282 |
Farmers’ market coverage (G2) | 0.372 | ||
Public toilet coverage (G3) | 0.209 | ||
Garbage collection station coverage (G4) | 0.137 |
Control Boundaries | Reference Data | Monitoring and Early Warning Rules |
---|---|---|
Urban development boundaries | Various boundaries, complete survey maps, and aerial photo enforcement polygons | Prohibit crossing. Construction must be confined within the urban development boundaries. A red warning will be issued if crossing is detected. |
Permanent bare cropland protection boundaries and ecological conservation boundaries | Prohibit occupation. A red warning will be issued if occupation is detected. | |
Nature reserve boundaries | Prohibit occupation. The core protection areas and general control areas are managed separately. A red warning will be issued if occupation is detected. | |
Historical protection zone boundaries, urban green space boundaries, urban surface water protection and control zone boundaries, and urban infrastructure land control zone boundaries | Strictly controlled occupation. A red warning will be issued if occupation or land misuse is detected. |
Indicators | Indicator and Data Source | Monitoring and Early Warning Rules |
---|---|---|
Master planning indicators |
| The corresponding warning rules can be developed based on the actual application of each indicator. The rules provided in this research are for reference only.
|
Detailed planning indicators |
| Each indicator follows the corresponding early warning rules. The following are some indicator rules:
|
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Share and Cite
Tang, M.; Dai, W.; Yin, C.; Hu, B.; Chen, J.; Liu, H. Spatial Planning Data Structure Based on Blockchain Technology. ISPRS Int. J. Geo-Inf. 2024, 13, 290. https://doi.org/10.3390/ijgi13080290
Tang M, Dai W, Yin C, Hu B, Chen J, Liu H. Spatial Planning Data Structure Based on Blockchain Technology. ISPRS International Journal of Geo-Information. 2024; 13(8):290. https://doi.org/10.3390/ijgi13080290
Chicago/Turabian StyleTang, Minwen, Wujiao Dai, Changlin Yin, Bing Hu, Jun Chen, and Haoming Liu. 2024. "Spatial Planning Data Structure Based on Blockchain Technology" ISPRS International Journal of Geo-Information 13, no. 8: 290. https://doi.org/10.3390/ijgi13080290