Related Authors: Corey Kewei Xu – HKUST（GZ）
Keywords: Fairness, Accountability, and Transparency, Public policy, Spatial data science, Urban Planning
- Spatial data brings an important dimension to AI’s quest for algorithmic transparency. (抽象空间数据为人工智能寻求算法透明性带来了一个重要的维度)
- For example, data driven computer-aidedpolicy-decisions（计算机辅助政策决策） use measures of segregation (e.g., dissimilarity in-dex) or income-inequality (e.g., Gini index), and these measures areaffected by space partitioning choice.（空间划分选择）
- This may lead policymakersto underestimate（低估） the level of inequality or segregation within aregion.
- The problem stems from（源于） the fact that many segregation based analyses use aggregated census data（汇总的人口普查数据） but do not report result sensitivity to choice of spatial partitioning (e.g., census block, tract).
- Currently, issues such as reducing global inequality is part of theUN Sustainability Development Goals (SDGs)
case studies（ with census data and census based synthetic micro-population data） （using ArcGIS Pro）
- Computationally, this work was done in Python using the propri-etary library “ArcPy” from the company Esri; NumPy and pandaswere used for data processing.（避免数值溢出）
- values of many measures (e.g., Gini index, dissimilarity index) diminish monotonically（单调递减） with increasing spatial-unit size （空间单元大小的增加）in a hierarchical space partitioning（分层空间分区） (e.g., block, block-group, tract),
- however， the ranking based on spatially（存在于空间地的） aggregated measures remain sensitive to the scale of spatial partitions (e.g., block, block group).
- This paper highlights the need for social scientists to report how rankings of inequality are affected by the choice of spatial partitions（空间分区选择）.
- Our findings highlight the importance of collecting and using fine-scale data to inform policies that address various social equity issues.
- We highlight the spatial dimension of algorithmic transparency with findings that reinforce the need to report the sensitivity of results to the choice of spatial partitioning.（空间划分的选择，应该进行敏感性分析）
- Beyond MAUP, we show theoretically that some measures (e. G., gini index, index of dissimilarity) of inequality decrease monotonically with increasing scale of analysis.（划分空间越大，这些值越小）
- We provide formal proofs on the upper and lower bound of the IOSR.（给出了形式证明）（收入五分位数份额比率（IQSR）：IQSR是最高20%的收入者获得的总收入与最低20%的收入者获得的收入之比。）
- case study supports the theoretical results
- using a synthetic house- hold level dataset
- using a 2010 American Community Survey dataset
- on various income inequality measures (Gini Index, IOSR, Theil, and Atkinson)
- have broad implications for equity related policy making
- Policymakers should be cautious about the unit of analysis when using existing equity indexes
- should conduct sensitivity analysis with different spatial partitioning choices
- plan to analyze the sensitivity of other measures of income inequality and segregation to choice of spatial partitioning.
- investigate computational methods to address the spatial dimensions of algorithmic transparency.(解决算法透明度的空间维度)
- In addition, we plan to consider other aggregation problems such as boundary effects（边界效应）. Computationally, random partitioning techniquescan(随机分区算法) be improved to reduce complexity and produce more optimal zones.
- Monte Carlo simulations can be used to estimate the effects of space partitioning for different zones.（蒙特卡罗模拟估计不同分区的空间划分效果）
- Did not read the mathematical proofs very carefully.
- I am interested in algorithmic transparency and measures of income inequality and segregation to choice of spatial partitioning.
- In terms of research methodology, I need to learn the processing of geographic and spatial data（ArcPy、ArcGIS Pro）, as well as the systematic study of spatial spillover effects, boundary effects and other spatial measurement theories.
- It is important to take notes and think while reading the literature.