30 1 118 ~ 131 2014 3 EARTHQUAKE RESEARCH IN CHINA Vol. 30 No. 1 Mar. 2014 2014 30 1 118 ~ 131 650224 6 GIS - 2013 3 3 5. 5 0. 89 1001-4683 2014 01-118-14 P315 A 0 1481 20 500 6. 5 2012 1991 2012 10 1 2013-06-17 2013-12-14 201108013 CEA_EDEM-201205 1980 Email 674714414@ qq. com
1 119 Clark 1951 Martin 1989 2004 2002 2003 2005-1999 2007 2012 2002 2004 2006 2004 Harvey 2000 2005 2003 2003a 2003b 2004 1983 1 1. 1 39. 4 10 4 km 2 6 16 129 1382 4596. 65 3 94% 1. 2 6 DEM 4 - GIS 1 1. 3 1 1. 4 - A = a 1 P b 1
120 30 1 1 / 1 1 5 2010 2 1 5 2010 3 2010 2012 4 2012 2012 2013 5 DEM 30m 2009 ASTER2009 6 1km 2008 7 1km 2008 8 30m 2008
1 121 P A 1 2 - - 2007 2012 - P = a Au b j j + D r A ri i P A a b D r A ri i a 1 P = M i P r + P u 1 P M i P u P r P r = D r1 A 1 + D r2 A 2 + D r3 A 3 2 A 1 A 2 A 3 P r b 1 D r1 D r2 D r3 P u = λα j A b uj 3 P u λ A a b 2 4 2. 1 2 2 2 26 4000 38. 2113% 1000 ~ 3500m 0. 02%
122 30 2 2 10 4 km 2 /10 4 km 2 / km 2 % 11 0. 124752 895 7174 496673 398 1. 1388 12 0. 959782 8565 8924 6397589 667 14. 6686 13 0. 05759 430 7467 417387 725 0. 957 14 0. 066296 529 7979 485122 732 1. 1123 21 0. 080034 423 5285 94551 118 0. 2168 22 0. 474347 2505 5281 1996883 421 4. 5785 23 0. 080999 462 5704 253574 313 0. 5814 24 0. 053124 282 5308 35483 67 0. 0814 31 0. 190102 749 3940 532596 280 1. 2211 32 1. 258775 5023 3990 2922401 232 6. 7006 33 0. 132859 741 5577 111839 84 0. 2564 34 0. 067235 284 4224 37175 55 0. 0852 41 0. 430807 1340 3110 277488 64 0. 6362 42 7. 379683 35149 4763 8798335 119 20. 1731 43 0. 417209 2131 5108 137752 33 0. 3158 44 0. 22264 1081 4855 244600 110 0. 5608 51 0. 414914 1694 4083 625334 151 1. 4338 52 13. 302969 56774 4268 16665610 125 38. 2113 53 0. 335009 1024 3057 150683 45 0. 3455 54 0. 148584 452 3042 107435 72 0. 2463 62 4. 707631 13959 2965 2707685 58 6. 2083 63 1. 796032 438 244 90019 5 0. 2064 64 0. 049564 16 323 10024 20 0. 023 72 0. 013916 7 503 7639 55 0. 0175 73 0. 102804 16 156 8063 8 0. 0185 74 0. 145331 8 55 2361 2 0. 0054
1 123 3 y R 2. 2 4 3 4
124 30 3 10 4 km 2 /10 4 km 2 / km 2 % < 5 1 10. 13119382 57894 5714 21593119 213 47. 0267 5 ~ 10 2 4. 681811376 20891 4462 7346675 157 16 10 ~ 15 3 6. 1451848 26002 4231 5895282 96 12. 8391 15 ~ 20 4 6. 336333948 24883 3927 4555808 72 9. 9219 20 ~ 25 5 4. 865733767 17092 3513 3392482 70 7. 3883 25 ~ 30 6 3. 052198708 8570 2808 1658066 54 3. 611 30 ~ 45 7 3. 051071715 5642 1849 1460237 48 3. 1802 45 ~ 60 8 0. 152132899 108 710 15065 10 0. 0328 > 60 9 0. 001520421 0 0 0 0 3 9 0 ~ 30 60% 5 10 10 4 km 2 21593119 47. 0267% 30 4% 60 5 y R 3 5 2. 3 2007 ArcGIS DEM 11 6 4 4 11 500m
1 125 60% 1000m 10% 2500m 6 4 m 10 4 km 2 /10 4 km 2 / km 2 % < 100 1 1. 098897 5146 4683 9558997 870 20. 7923 100 ~ 200 2 1. 970994 8652 4390 6364375 323 13. 8435 200 ~ 300 3 2. 92028 13691 4688 4987279 171 10. 8481 300 ~ 400 4 3. 599602 16302 4529 5206284 145 11. 3245 400 ~ 500 5 4. 104653 17561 4278 4887002 119 10. 63 500 ~ 600 6 4. 294183 17474 4069 4199741 98 9. 1351 600 ~ 1000 7 13. 527477 53049 3922 5906023 44 12. 8465 1000 ~ 1500 8 6. 466778 24372 3769 4079635 63 8. 8739 1500 ~ 2000 9 1. 827298 4421 2419 775585 42 1. 687 2000 ~ 2500 10 0. 262596 440 1676 7868 3 0. 0171 > 2500 11 0. 018116 10 552 865 5 0. 0019 6 > 2500m 5 / km 2 2. 4
126 30 7 y R 3 ArcGIS 5 5 10 4 km 2 /10 4 km 2 / km 2 % 11 12 6. 173211 54862 9810 9696926 212 21. 6235 21 7. 513518 13078 1741 4537263 60 10. 1178 22 7. 845554 29113 3711 4916429 63 10. 9633 23 24 4. 176212 15066 3608 3626033 87 8. 0858 31 5. 044992 22616 4483 4184080 83 9. 3302 32 2. 648744 10396 3925 3073452 116 6. 8536 33 0. 253195 446 1761 239892 95 0. 5349 41 42 43 44 46 0. 253786 628 2475 9638 4 0. 0215 51 52 53 0. 181579 14652 38293 14531013 6014 32. 4032 64 65 66 0. 195426 83 425 29725 15 0. 0663 5 2000km 2 14531013 32% 6014 / km 2 0. 08% 2. 5 W 1 0. 2741 0. 2179 0. 1937 0. 3143 W 2 M i
1 127 M i = n i = 1 W 1 W 2 M i W 1 W 2 3-3. 1 Clark 1951 ρ r = ρ 0 e - r / r 0 λ ρ r ρ 0 r r 0 16 129 6 6 λ R D 1 0. 25 0. 5 2 0. 25 0. 25 3 3 0. 5 0. 25 3. 2 48 1 48 48 Matlab = 6832. 16 D r2 = 3832. 24 D r3 = 1174. 27 D r1 3. 3 1 2 3 ArcGIS 8
128 30 8 4 2013 3 3 5. 5 ArcGIS 7 9 7 5. 5 * Ⅶ 26410 17414 22819 24885 Ⅵ 171000 81432 98457 116703 197410 98846 126276 141588 % 139. 43 69. 81 89. 19 * 2013 3 3 5. 5 89. 19%
1 129 9 2013 3 3 5. 5 5 1 GIS 4 2-2013 3 3 5. 5 3
130 30 1999 Nordbee1-Dutton 12 2 198 ~ 203 2012 2007 7 3 273 ~ 284 2007 16 2 265 ~ 268 2004 24 1 105 ~ 108 2007 62 10 1074 ~ 1082 2012 2010 2013 2012 2002 RS GIS 17 5 734 ~ 738 1983 2002 27 6 301 ~ 305 2003 32 4 344 ~ 348 2005 GIS 45 9 1189 ~ 1192 2003 58 1 17 ~ 24 2003a GIS 58 1 25 ~ 33 2003b. 13 3 62 ~ 67 1991 7 1 89 ~ 94 2012 34 4 783 ~ 791 2004 59 2 283 ~ 292 2004 GIS 20 4 71 ~ 74 2004 30 3 91 ~ 93 2012 1992 ~ 2010 2006 25 3 62 ~ 69 2005 60 2 266 ~ 276 Clark C 1951 Urban population densities Joumal of the Royal Statistical Society Series A Genera1 114 4 490 ~ 496. Harvey J T 2000 Small area population estimation using satellite imagery Statistical Transition 4 4 611 ~ 633. Martin D 1989 Mapping population data from zone centroid locations Transactions of the Institute of British Geographers 14 90 ~ 97.
1 131 Simulation of spatial distribution of the population of Yunnan based on the integration of multi-resource data Cao Yanbo Li Yongqiang Li Min Li Zhaolong Wu Yanmei Li Zhirong Earthquake Administration of Yunnan Province Kunming 650224 China Abstract According to the theory of integration of multi-resource data we refer to the demographic data of Yunnan Province released in 2011 after the Sixth National Population Census to set the residential area as an indicator of population distribution in Yunnan. Then by using GIS to analyze the relation between the population distribution and the factors such as landform topographic slope amplitude of landform relief and land-use we get the weighing coefficient of the impact factor of population. Finally we use the unified model of population and area to estimate the earthquake-affected population by way of kilometer-grid transformation and check the precision of the result. To test the practicability of our estimation we calculate with various methods the population of the disaster area of the Eryuan Yunnan M5. 5 earthquake on March 3 2013. By comparing the results from these methods we find that the estimated population through the kilometer-grid transformation is highly-related with the real population the correlation coefficient is over 0. 89. So the estimated population is reliable for the quick judgment of the disaster condition. Key words Earthquake Demographic data Space-based transformation Residential area Kilometer grid