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【正文】 ma (%) Median ine ($) High needs Population (%) Consolidating nonspatial variables 0 25 50 751 2 .5M i l e s177。Socioe cono mic Di sadva ntage Scor es (Fa ctor 1 )L e g e n dF AC T OR1 2. 0 1130 0 0. 4628 0 0 0. 4 6279 9 0. 1000 000 . 10 0001 0. 9 3330 00 . 93 3301 1. 8 8860 01 . 88 8601 3. 1 6170 03 . 16 1701 5. 8 7210 0C ou nt y Boun d ary0 25 50 751 2 . 5Mi l e s177。High H ealth Care Need Score s (Fac tor 3)L e g e n dF AC T OR3 7. 3 7680 0 1. 7604 0 0 1. 7 6039 9 0. 7023 0 0 0. 7 0229 9 0. 0231 000 . 02 3101 0. 7 1520 00 . 71 5201 1. 6 9740 01 . 69 7401 5. 7 8610 0C ou nt y Boun d aryIntegrating Spatial and Nonspatial Factors ? Geographic Area ? Primary Indicator (spatial accessibility score) 1/3500 ? Primary Indicator (spatial accessibility score) 1/3000 AND secondary indicator (factor 3) 1 standard deviation above mean ? Population Group ? Primary Indicator (factor 1) 1 standard deviation above mean ? Primary Indicator (factor 1) 190。 primary care access: significant in breast and lung cancer ? Others: marginally or not significant ? Effects of urbanrural location “urban disadvantage”? 0 . 60 . 70 . 80 . 911 . 1C h i c a g o C h i c a g oS u b u r b sO t h e rM e t r oL a r g eT o w nR u r a lBre a s tC o l o re ct a lLungPro s t a t ePublic policy implication How people in particular geographic contexts interact with local health care providers? Concluding remarks ? Rise of Computational Social Sciences (CSS) and Spatiallyintegrated Social Sciences (SSS) ? Trends in social sciences ? Michael Batty: “to do good social science that is policy relevant, quantitative methods are essential and such methods, and the theory behind their practice, must be spatial.”
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