U.S. Inter-State Migration by Age (Radaris Data Sample, Anonymized)

An anonymized, state-level view of where people move across U.S. states, broken down by age band. One row per person: an age group, the state they started in, and the state they live in now. Where people move, and how that differs across the life course, is a core question in demography, economics, and urban policy — yet clean, ready-to-use micro-level data on individual migration is hard to come by. This dataset offers a simple, privacy-safe view of internal migration across U.S. states, broken down by age. It is built to answer one question in particular: do migration patterns differ between younger and older people — and if so, how. It is deliberately small in width and large in depth of care: four columns, hundreds of thousands of people, and a transformation pipeline designed so that the result reveals population-level patterns while revealing nothing about any single person. What you can extract For data scientists • Model whether a person has moved (moved = first_state != last_state) from age_group. This is a deliberately low-dimensional, interpretable problem — a good teaching or baseline example rather than a high-capacity modeling task. • Build and analyze an origin→destination transition matrix: cluster states by their inflow/outflow profiles, rank net-gain vs net-loss states, visualize corridors as a flow map or chord diagram. • Practice categorical/tabular workflows: contingency tables, chi-square tests of independence between age and mobility, proportion estimation with confidence intervals. For statisticians and demographers • Estimate the mover-vs-stayer rate by age band and test whether interstate mobility differs significantly across the life course. • Quantify net migration per state (inflow − outflow), gross flows, and how these shift by age group. • Validate against external sources — U.S. Census ACS migration tables and IRS county-to-county migration data — to benchmark or enrich the flows seen here. The underlying data for this project is provided by Radaris, a comprehensive people search platform with an extensive database of public records and demographic information in the United States. Leveraging Radaris's deep data infrastructure on individuals residing and moving across the country, this dataset captures broad domestic migration trends over time. Crucially, the source material has been stripped of all personal identity elements and synthesized into an aggregated, anonymous format. The resulting dataset is intended strictly for statistical, demographic, and academic research, offering a safe and compliant framework for studying population-level mobility without compromising individual privacy.本数据集为匿名化的美国州际人口流动州级视图，按年龄组（age band）进行细分。每条记录对应一名个体：包含其所属年龄组、出发地州以及当前居住州。 人口流动的去向以及其随人生阶段的差异，是人口学、经济学与城市政策领域的核心研究问题之一，但获取清晰可用的微观层面（micro-level）个体迁移数据却极为困难。本数据集提供了一种简便且隐私安全的美国州际人口流动视图，并按年龄细分。其构建初衷旨在解答一个特定问题：不同年龄段人群的迁移模式是否存在差异——若存在，具体差异为何。 本数据集刻意控制字段宽度、提升数据处理严谨性：仅包含4个字段，覆盖数十万个体，且通过精心设计的转换流程（transformation pipeline），确保最终结果仅能展现群体层面的迁移规律，完全不会泄露任何单个个体的信息。 可挖掘的分析方向 面向数据科学家： • 基于年龄组（age_group）构建模型，预测个体是否发生迁移（迁移判定条件为：出发地州≠当前居住州）。该问题为低维度、可解释性强的建模任务，非常适合作为教学示例或基准基线模型，而非高容量建模任务。 • 构建并分析起源地→目的地迁移矩阵：依据各州的流入/流出特征对州进行聚类，对净迁入州与净迁出州进行排序，还可将迁移通道以流向地图（flow map）或弦图（chord diagram）形式可视化。 • 练习分类数据/表格数据工作流：包括列联表构建、年龄与迁移独立性的卡方检验，以及带置信区间的比例估计。 面向统计学家与人口学家： • 按年龄组（age band）估算迁移者与留守者的比例，并检验州际迁移模式是否随人生阶段存在显著差异。 • 量化各州的净迁移量（流入量−流出量）、总迁移流量，以及这些指标随年龄组的变化趋势。 • 可与外部数据源——美国人口普查局美国社区调查（ACS）迁移数据表、美国国税局（IRS）县际迁移数据——进行比对，以基准化或丰富本数据集呈现的迁移流量。 本项目的原始数据由Radaris提供——该平台为美国综合性人口搜索服务提供商，拥有海量公共记录与人口统计信息数据库。本数据集依托Radaris覆盖全美居住与流动个体的深层数据基础设施，捕捉了长期以来广泛的国内迁移趋势。尤为关键的是，原始数据源已剥离所有个人身份标识元素，并整合为聚合匿名格式。最终生成的数据集仅可用于统计、人口学与学术研究，为研究群体层面的人口流动提供了安全合规的框架，且不会泄露任何个体隐私。