Cartography and Geographic Information Science ( IF 2.354 ) Pub Date : 2021-09-01 , DOI: 10.1080/15230406.2021.1960609 Yan Lin 1 , Christopher Lippitt 1 , Daniel Beene 1, 2 , Joseph Hoover 3
ABSTRACT
GIS-based spatial access measures have been used extensively to monitor social equity and to help develop policy. However, inherent uncertainties in the road datasets used in spatial access estimates remain largely underreported. These uncertainties might result in unrecognized biases within visualization products and decision-making outcomes that strive to improve social equity based on seemingly egalitarian accessibility metrics. To better understand and address these uncertainties, we evaluated variations in travel impedance for car and bus transportation using proprietary, volunteer-information-based, and free (non-volunteer-information-based) street networks. We then interpreted the measured variations through the lens of street data uncertainty and its propagation in a common E2SFCA model of spatial accessibility. Results indicated that travel impedance disagreement propagates through the modeling process to effect agreement of spatial access index (SPAI) estimates among different street sources, with larger uncertainties observed for bus travel than car travel. Higher impedance coefficients (β), a model parameter, reduced the impact of street-source variations on estimates. Less urbanized regions were found to experience higher street-source variations when compared with the core-metropolitan area. We also demonstrated that a relative spatial access measure – the spatial access ratio (SPAR) – reduced uncertainties introduced by the choice of street datasets. Careful selection of reliable street sources and model parameters (e.g. higher β), as well as consideration of the potential for bias, particularly for less urbanized areas and areas reliant on public transportation, is warranted when leveraging SPAI to inform policy. When reliable/accurate road network data are not accessible or data quality information is not available, the SPAR is a suitable alternative or supplement to SPAI for visualization and analyses.
中文翻译:
旅行时间不确定性对空间可达性建模的影响:街道数据源的比较
摘要
基于 GIS 的空间访问措施已被广泛用于监测社会公平和帮助制定政策。然而,空间访问估计中使用的道路数据集的固有不确定性在很大程度上仍被低估。这些不确定性可能会导致可视化产品和决策结果中出现未被承认的偏见,这些结果旨在基于看似平等的可访问性指标来改善社会公平。为了更好地理解和解决这些不确定性,我们使用专有的、基于志愿者信息的和免费(非基于志愿者信息的)街道网络评估了汽车和公共汽车交通的旅行阻抗变化。然后,我们通过街道数据不确定性及其在空间可达性的常见 E2SFCA 模型中的传播来解释测量的变化。结果表明,旅行阻抗不一致通过建模过程传播,以影响不同街道来源之间空间访问指数 (SPAI) 估计的一致性,观察到公共汽车旅行的不确定性大于汽车旅行。较高的阻抗系数 (β)(模型参数)降低了街道源变化对估计值的影响。与核心都市区相比,城市化程度较低的地区的街道源变化更大。我们还证明了相对空间访问量度——空间访问率 (SPAR)——减少了街道数据集选择带来的不确定性。仔细选择可靠的街道来源和模型参数(例如更高的 β),并考虑潜在的偏差,特别是对于城市化程度较低的地区和依赖公共交通的地区,在利用 SPAI 为政策提供信息时是有必要的。当可靠/准确的道路网络数据不可访问或数据质量信息不可用时,SPAR 是 SPAI 的合适替代或补充,用于可视化和分析。