GIS-Based and Statistical Approaches in Archaeological Predictive Modelling (NE Romania)
Abstract
1. Research Aims
2. Introduction
3. Archaeological Background and Study Area
4. Materials and Methods
5. Results and Discussion
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Conditioning Factor | Class | No. of Pixels in Domain | Pixels % | Sites Pixels | Sites Pixels % | Frequency Ratio (FR) |
---|---|---|---|---|---|---|
Soils (type) | 1 Luvisols | 14,818,636 | 4.66 | 74,529 | 3.67 | 0.04 |
2 Cambicc Chernozem | 60,267,943 | 18.95 | 527,989 | 26.02 | 0.07 | |
3 Clay Chernozem | 15,907,541 | 5.00 | 10,129 | 0.50 | 0.01 | |
4 Chernozem | 74,671,552 | 23.47 | 371,852 | 18.33 | 0.04 | |
5 Entic Aluviosols | 24,407,123 | 7.67 | 107,480 | 5.30 | 0.04 | |
6 Phaeozems | 40,426,881 | 12.71 | 300,325 | 14.80 | 0.06 | |
7 Antrosols | 33,237,067 | 10.45 | 318,433 | 15.69 | 0.08 | |
8 Gleysols | 13,869,069 | 4.36 | 2134 | 0.11 | - | |
9 Regosols | 1,888,710 | 0.59 | - | - | - | |
10 Solonetz | 115,847 | 0.04 | - | - | - | |
11 Rendzina | 5,965,903 | 1.88 | 81,585 | 4.02 | 0.11 | |
12 Aluviosols | 13,567,024 | 4.27 | 82,384 | 4.06 | 0.05 | |
13 Stagnosol | 16,705,752 | 5.25 | 85,916 | 4.23 | 0.04 | |
14 Bare rock | 1,158,248 | 0.36 | 31,383 | 1.55 | 0.22 | |
15 Phaeozems | 1,089,677 | 0.34 | 34,935 | 1.72 | 0.26 | |
HLI (Heat Load Index) | 0.097–0.59 | 4,898,405 | 1.54 | 54,351 | 2.68 | 0.28 |
0.59–0.64 | 35,352,785 | 11.12 | 252,849 | 12.46 | 0.18 | |
0.64–0.67 | 154,500,235 | 48.58 | 948,083 | 46.74 | 0.16 | |
0.67–0.71 | 108,268,154 | 34.04 | 641,492 | 31.62 | 0.15 | |
0.71–1.04 | 15,031,047 | 4.73 | 131,846 | 6.50 | 0.22 | |
SPI (Slope Position Classification) | 1 Valley | 27,167 | 0.01 | 94 | 0 | 0.09 |
2 Toe slope | 162,659 | 0.05 | 1290 | 0.06 | 0.21 | |
3 Flat | 175,545,165 | 55.19 | 1,039,526 | 51.24 | 0.15 | |
4 Midslope | 142,172,529 | 44.70 | 986,466 | 48.63 | 0.18 | |
5 Upper slope | 115,513 | 0.04 | 1115 | 0.05 | 0.25 | |
6 Ridges | 27,593 | 0.01 | 130 | 0.01 | 0.12 |
Class | Pixel Number | Area (%) | Number of Sites | Sites (%) |
---|---|---|---|---|
Very high | 114,551,423 | 36 | 23 | 23 |
High | 146,075,657 | 46 | 41 | 41 |
Medium | 54,695,517 | 17.1 | 33 | 33 |
Low | 2,766,013 | 0.9 | 3 | 3 |
Total | 318,088,610 | 100 | 100 | 100 |
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Nicu, I.C.; Mihu-Pintilie, A.; Williamson, J. GIS-Based and Statistical Approaches in Archaeological Predictive Modelling (NE Romania). Sustainability 2019, 11, 5969. https://doi.org/10.3390/su11215969
Nicu IC, Mihu-Pintilie A, Williamson J. GIS-Based and Statistical Approaches in Archaeological Predictive Modelling (NE Romania). Sustainability. 2019; 11(21):5969. https://doi.org/10.3390/su11215969
Chicago/Turabian StyleNicu, Ionut Cristi, Alin Mihu-Pintilie, and James Williamson. 2019. "GIS-Based and Statistical Approaches in Archaeological Predictive Modelling (NE Romania)" Sustainability 11, no. 21: 5969. https://doi.org/10.3390/su11215969
APA StyleNicu, I. C., Mihu-Pintilie, A., & Williamson, J. (2019). GIS-Based and Statistical Approaches in Archaeological Predictive Modelling (NE Romania). Sustainability, 11(21), 5969. https://doi.org/10.3390/su11215969