跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文,不再错过。

覆盖计算机全学科,以 AI 为快捷入口。关注方向、作者和关键词,把发现变成持续的研究积累。

采集已暂停
正在读取账号与研究记录…

全站更新概况

+0新增论文
+0其中 AI 相关
+0已有论文修订
+0新增 AI 导读

arXiv 最近完成扫描:等待完成 · 全站最近入库:2026-11-02 00:39各信源进度 →

统计口径与数据新鲜度

统计 2026-11-03 10:03 — 2026-11-04 10:03(北京时间) · 点击数字查看对应简报;AI 相关包含在新增论文内。

最近完成检查:尚未完成最近发现新增版本:2026-11-02 00:39库内论文最新更新时间:2026-10-30 01:59

每分钟刷新已保存的数据。空检查不代表采集异常;新收录可能包含历史论文。AI 导读按新增版本计数。

8,677 已收录论文7,675 AI 相关1,866 有修订112 HF 社区发现

浏览论文库

时间范围跟随排序:版本更新、本站收录或首次发表。最新收录可能包含历史论文。

找到 1 篇论文 · 按版本更新时间排序等待首次采集
版本更新 · v3arXiv:2512.10753

Quantifying displacement: an urban expansion consequence via persistent homology

Rita Rodríguez Vázquez, Manuel Cuerno

Population displacement is a housing-related involuntary residential dislocation. It has become increasingly widespread in many cities, particularly in neighbourhoods undergoing rapid economic and demographic change, and measuring it is essential to assess the social consequences of urban transformation and housing market pressures. Despite its relevance, quantifying displacement presents difficulties due to limited replicability across cities and time periods and the need to analyse long time spans: displacement is a gradual process, impossible to capture in one data snapshot. We introduce a novel tool to overcome these difficulties. Using publicly available address change data, we construct four cubical complexes simultaneously incorporating geographical and temporal information of people moving, and analyse using Topological Data Analysis tools. Finally, we demonstrate this method through a 20-year case study in Madrid, Spain. The results demonstrate its ability to reveal patterns consistent with population displacement and identify the neighbourhoods and years potentially affected--patterns not observable from yearly maps of the raw address change data.

尚无 AI 解读,展示原文摘要

cs.CG · cs.SI
arXiv ↗PDF ↗