| 000 | 02603nab a2200325 4500 | ||
|---|---|---|---|
| 999 |
_c11634 _d11634 |
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| 003 | OSt | ||
| 005 | 20210413120216.0 | ||
| 007 | cr aa aaaaa | ||
| 008 | 210413b ||||| |||| 00| 0 eng d | ||
| 100 |
_aLogan, TM _945829 |
||
| 245 | _aEvaluating urban accessibility: leveraging open-source data and analytics to overcome existing limitations | ||
| 260 |
_bSage, _c2019. |
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| 300 | _aVol 46, Issue 5, 2019,(897-913 p.) | ||
| 520 | _aWe revisit the standard methodology for evaluating proximity to urban services and recommend enhancements to address existing limitations. Existing approaches often simplify their measure of proximity by using large areal units and by imposing arbitrary distance thresholds. By doing so, these approaches risk overlooking vulnerable, access-poor populations – the very populations that such studies are often trying to identify. These limitations are primarily motivated by computational constraints. However, recent advances in computational power, open data, and open-source analytics permit high-resolution proximity analyses on large scales. Given the impetus for equitable accessibility in our communities, this is of fundamental importance for researchers and practitioners. In this paper, we present an approach that leverages these open source advances to (a) measure proximity using network distance at the building level, (b) estimate population at that level, and (c) present the resulting distributions so vulnerable populations can be identified. Using three cities and modes of transport, we demonstrate how the approach enhances existing measures and identifies service-poor populations where the previous methods fall short. The proximity results could be used alone, or as inputs to access metrics. Our collating of these components into an open source code provides opportunities for researchers and practitioners to explore fine-resolution, city-wide accessibility across multiple cities and the host of questions that follow. | ||
| 650 |
_aSpatial accessibility, _945830 |
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| 650 |
_aproximity, _939928 |
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| 650 |
_a walking, _942186 |
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| 650 |
_acycling, _937551 |
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| 650 |
_ahealth care, _945831 |
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| 650 |
_agreen space, _945832 |
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| 650 |
_afood deserts _945833 |
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| 700 |
_aWilliams, TG _933892 |
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| 700 |
_aNisbet, AJ _945834 |
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| 700 |
_aLiberman, KD _945835 |
||
| 700 |
_aZuo, CT _945836 |
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| 700 |
_a Guikema, SD _945837 |
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| 773 | 0 |
_011590 _915512 _dSage 2019. _t Environment and Planning B: Urban Analytics and City Science |
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| 856 | _uhttps://doi.org/10.1177/2399808317736528 | ||
| 942 |
_2ddc _cART |
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