Mapping Gross Domestic Product Distribution at 1 km Resolution across Thailand Using the Random Forest Area-to-Area Regression Kriging Model
<p>Elevation and regional divisions (<b>a</b>), LULC (<b>b</b>), agriculture statistical GDP (<b>c</b>), and non-agriculture statistical GDP (<b>d</b>) of Thailand at the province level. (GDP in constant 2011 international USD).</p> "> Figure 2
<p>The result of KDE bandwidth determination.</p> "> Figure 3
<p>Tuning curves of random forest modeling for agricultural and non-agricultural GDP with four different scenarios. (The horizontal coordinate represents mtree).</p> "> Figure 4
<p>Flowchart of the GDP spatialization.</p> "> Figure 5
<p>The spatial distribution of downscaled GDP using RF, SVR, and MLR methods.</p> "> Figure 6
<p>The spatial distribution of downscaled GDP by using RFATARK, SVATARK, and MLATARK methods.</p> "> Figure 7
<p>The linear relationship between statistical GDP and spatialized GDP at the provincial scale. GDP derived by (<b>a</b>) RFATARK, (<b>b</b>) SVATARK, (<b>c</b>) MLATARK using MC group of auxiliary variables, and (<b>d</b>) G_GDP product. The black dashed line represents the 1:1 line, and the orange line represents the line fitted through the scatter points.</p> "> Figure 8
<p>The spatial distribution of downscaled GDP by using (<b>a</b>) RFIDW, (<b>b</b>) RF_MC, and (<b>c</b>) XGBoost methods and MC group auxiliary variables.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Materials
2.3. Data Processing
2.3.1. NTL Data Processing
2.3.2. OSM Data Processing
2.3.3. Other Processing
2.4. Downscaling Methodology
2.4.1. Downscaling Model
2.4.2. Downscaling Strategy
3. Results
3.1. Gridded GDP Mapping
3.2. Accuracy Assessment
4. Discussion
4.1. GDP Spatialization for Different Sectors
4.2. Data Used for GDP Spatialization
4.3. GDP Spatialization Methods
4.4. Impact of Residual Predictions
4.5. Additional Factors Potentially Impacting Accuracy
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Type | Dataset | Description | Source |
---|---|---|---|
① | NTL | Annual composited NPP/VIIRS nighttime light data Spatial resolution: 15 arc-seconds | Earth Observation Group at Payne Institute for Public Policy, Colorado School of Mines |
Vegetation index | Annual synthetic NDVI, EVI, and MODIS near-infrared vegetation reflectance index Spatial resolution: 250 m | Maximum value composite based on MODIS product (MOD13A3) or MODIS-based data calculated by MODIS Surface reflectance product (MOD43) | |
Land surface temperature (LST) | Annual synthetic LST Spatial resolution: 1000 m | Maximum value composite based on MODIS product (MYD11A1) | |
LULC | Finer Resolution Observation and Monitoring—Global Land Cover Spatial resolution: 30 m | FROM-GLC at Tsinghua University | |
② | Terrain data: digital elevation model (DEM) | ASTER/GDEM Spatial resolution: 30 m | Earth Remote Sensing Data Analysis Center of Japan |
Boundary information | Provincial boundaries | Database of Global Administrative Areas | |
Road, water, and point of interest (POI) | Road network, water bodies, and water roads, and 13 types of POIs of Thailand | Open Street Map (OSM) from Geofabrik GmbH | |
③ | GDP statistical and census data | Total GDP and population of the 77 provinces in Thailand | Office of the Thailand Economic and Social Development Council |
Gridded GDP data | GDP of each grid cell Spatial resolution: 30 arc-seconds | Kummu et al. (2018) | |
Population (Worldpop) | Grided population count datasets Spatial resolution: 100 m | University of Southampton |
Class | Content | Group | Weight (Class) | Weight (Group) |
---|---|---|---|---|
Major roads | Motorways, primary roads, secondary roads, tertiary roads. | Road1 | 0.148 | 0.263 |
Rail | Railways. | Road1 | 0.131 | |
Highway links | Roads that connect from one road to another. | Road2 | 0.133 | 0.247 |
Subway | Subways. | Road2 | 0.092 | |
Minor roads | Smaller local roads, roads in residential areas, streets. | Road3 | 0.147 | 0.251 |
Paths | Paths unsuitable for cars. | Road3 | 0.113 | |
Small roads | Paths for cycling, footpaths, gravel roads, etc. | Road4 | 0.135 | 0.239 |
Unknown | Unknown type of road or path. | Road4 | 0.101 | |
River | Large rivers. | Water1 | 0.137 | 0.231 |
Reservoir | Artificial lakes. | Water1 | 0.111 | |
Stream | Smaller rivers or streams. | Water2 | 0.124 | 0.294 |
Canal | Canals. | Water2 | 0.125 | |
Wetland | Swamp, bog, or marsh land. | Water3 | 0.153 | 0.204 |
Water | Unspecified bodies of water. | Water3 | 0.104 | |
Drain | Small drainage ditches or similar structures. | Water4 | 0.124 | 0.271 |
Dock | Docks. | Water4 | 0.122 |
Class | Content | Weight |
---|---|---|
Accommodation | Hotels, motels, guesthouses, hostel, etc. | 0.092 |
Catering | Restaurants, bars, cafes, etc. | 0.102 |
Health | Pharmacies, hospitals, veterinaries, etc. | 0.084 |
Leisure | Theaters, playgrounds, parks, cinemas, stadiums, etc. | 0.093 |
Fuel and parking | Gas stations, service areas, car parks, etc. | 0.056 |
Money | Banks, ATMs, etc. | 0.104 |
Public | Police posts, fire stations, post offices, libraries, schools, etc. | 0.064 |
Village and hamlet | Villages and hamlets. | 0.031 |
Tourism | Tourist attractions, museums, monuments, zoos, ruins, etc. | 0.077 |
Pofw | Buddhist temples, churches, synagogues, mosques, Muslim places, etc. | 0.050 |
Miscpoi | Toilets, fountains, fire hydrants, towers, etc. | 0.066 |
Shopping | Supermarkets, bakeries, malls, travel agencies, vending machines, etc. | 0.094 |
Transport | Railway stations, bus stops, subway stations, airports, etc. | 0.087 |
Model | Category | RMSE | MAE | R2 |
---|---|---|---|---|
RF | UMS | 7550.438 | 2198.005 | 0.578 |
UMC | 7093.521 | 2124.864 | 0.642 | |
MS | 7431.198 | 2163.931 | 0.598 | |
MC | 7065.877 | 2107.321 | 0.647 | |
Average | 7285.259 | 2148.53 | 0.616 | |
SVR | UMS | 8756.371 | 2597.505 | 0.391 |
UMC | 8424.893 | 2464.847 | 0.443 | |
MS | 8747.029 | 2559.936 | 0.393 | |
MC | 8414.596 | 2461.994 | 0.445 | |
Average | 8585.722 | 2521.071 | 0.418 | |
MLR | UMS | 17,259.112 | 4397.361 | 0.551 |
UMC | 16,309.466 | 4695.980 | 0.363 | |
MS | 51,242.087 | 13,478.187 | 0.607 | |
MC | 184,033.264 | 40,547.569 | 0.611 | |
Average | 67,210.980 | 15,779.77 | 0.533 | |
RFATARK | UMS | 987.344 | 326.345 | 0.981 |
UMC | 987.006 | 328.335 | 0.965 | |
MS | 420.958 | 196.956 | 0.993 | |
MC | 402.082 | 174.195 | 0.998 | |
Average | 699.348 | 256.458 | 0.984 | |
SVATARK | UMS | 1031.165 | 294.517 | 0.972 |
UMC | 1027.465 | 297.735 | 0.998 | |
MS | 936.841 | 308.826 | 0.977 | |
MC | 915.298 | 288.899 | 0.996 | |
Average | 977.692 | 297.494 | 0.986 | |
MLATARK | UMS | 9174.452 | 2782.196 | 0.328 |
UMC | 8771.900 | 2704.327 | 0.388 | |
MS | 9159.387 | 2779.289 | 0.331 | |
MC | 8363.801 | 2621.250 | 0.451 | |
Average | 8867.385 | 2721.766 | 0.375 | |
G_GDP | / | 36,042.271 | 13,657.988 | 0.877 |
Model | RMSE | MAE | R2 |
---|---|---|---|
RFIDW | 1528.675 | 460.424 | 0.578 |
RF_MC | 8673.488 | 2672.458 | 0.503 |
XGBoost | 8679.099 | 2615.605 | 0.504 |
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Jin, Y.; Ge, Y.; Fan, H.; Li, Z.; Liu, Y.; Jia, Y. Mapping Gross Domestic Product Distribution at 1 km Resolution across Thailand Using the Random Forest Area-to-Area Regression Kriging Model. ISPRS Int. J. Geo-Inf. 2023, 12, 481. https://doi.org/10.3390/ijgi12120481
Jin Y, Ge Y, Fan H, Li Z, Liu Y, Jia Y. Mapping Gross Domestic Product Distribution at 1 km Resolution across Thailand Using the Random Forest Area-to-Area Regression Kriging Model. ISPRS International Journal of Geo-Information. 2023; 12(12):481. https://doi.org/10.3390/ijgi12120481
Chicago/Turabian StyleJin, Yan, Yong Ge, Haoyu Fan, Zeshuo Li, Yaojie Liu, and Yan Jia. 2023. "Mapping Gross Domestic Product Distribution at 1 km Resolution across Thailand Using the Random Forest Area-to-Area Regression Kriging Model" ISPRS International Journal of Geo-Information 12, no. 12: 481. https://doi.org/10.3390/ijgi12120481
APA StyleJin, Y., Ge, Y., Fan, H., Li, Z., Liu, Y., & Jia, Y. (2023). Mapping Gross Domestic Product Distribution at 1 km Resolution across Thailand Using the Random Forest Area-to-Area Regression Kriging Model. ISPRS International Journal of Geo-Information, 12(12), 481. https://doi.org/10.3390/ijgi12120481