A discrete choice experiment to validate the use of areal wombling for detecting social boundaries

Data, code and materials from a discrete experiment to test the validity of an Bayesian areal wombling algorithm for predicting social boundaries. The experiment was conducted as a part of project ‘Life at the Frontier: Researching the Impact of Social Frontiers on the Social Mobility and Integration of Migrants’ (2020-2023; NordForsk/ESRC, project no 95193), and experiment data was collected in Rotherham (UK).<b>About the experiment</b>Each border on a map is assigned a boundary value based on how dissimilar the adjacent neighbourhoods are (higher = more dissimilar = more likely to be a social boundary).The experiment was carried out as follows:- We created three maps of the same area with different boundaries using the Bayesian areal wombling approach.- Map A contained the boundaries with the highest boundary values, whilst map C had the lowest boundary values. Map B contained boundaries that were in between.- During an interview, participants were then shown pairs of maps and asked which map in each pair best corresponds to local community boundaries.- The sequence and order of the maps shown were randomised.- Assuming that residents and experts can recognise (but not necessarily recall) social boundaries, we conjecture that participants would choose the map containing borders with higher boundary values.Hypothesis: We hypothesise that participants will agree with the predictions of the areal wombling algorithm and choose boundaries with higher boundary values.Null hypothesis: Participants are not more or less likely to choose boundaries with higher boundary values.Aside from testing a hypothesis, another motivation behind the study is to explore the feasibility of the method for future replications and follow-up research.<b>More information</b>This study was approved by the University of Sheffield ethics committee (application number 042378).Please read the README file for a more detailed description of the content of this repository.

本数据集包含用于检验贝叶斯区域边界探测（Bayesian areal wombling）算法预测社会边界有效性的离散实验相关数据、代码与研究材料。本实验作为「边境生活：探究社会边界对移民社会流动与融合的影响」（2020-2023；NordForsk/英国经济与社会研究理事会（ESRC），项目编号95193）项目的一部分开展，实验数据采集于英国罗瑟勒姆（Rotherham）。

<b>关于本实验</b>
每张地图上的边界均会基于相邻街区的差异程度被赋予边界值（数值越高代表差异越大，越有可能是社会边界）。本实验具体流程如下：
1. 我们采用贝叶斯区域边界探测方法，针对同一区域生成三张具有不同边界的地图。其中地图A包含边界值最高的边界，地图C包含边界值最低的边界，地图B的边界值介于两者之间。
2. 访谈过程中，向参与者展示成对的地图，并询问每一组地图中哪一张最贴合当地社区的实际边界。展示地图的顺序与组合均经过随机化处理。
3. 基于居民与专家能够识别（但未必能准确回忆）社会边界这一前提，我们推测参与者会选择边界值更高的地图所对应的边界。
研究假设：我们假设参与者会认可区域边界探测算法的预测结果，更倾向于选择边界值更高的边界。
原假设：参与者选择边界值更高的边界的概率与选择其他边界的概率无显著差异。
除验证研究假设外，本研究的另一目标是探索该方法在未来重复研究与后续拓展研究中的可行性。

<b>更多信息</b>
本研究已通过谢菲尔德大学伦理委员会审批（申请编号042378）。如需了解本仓库内容的详细说明，请查阅README文件。