Mobility Australia - New South Wales Monthly Trains Origin-Destination Flow (SA2) 2019

This dataset estimates human mobility through origin destination (OD) movement flow among the Statistical Area 2 (SA2) regions in New South Wales (NSW), connected by public transport (PT) networks. The SA2 regions of New South Wales connected by Sydney trains (T1-T9) and Metro services (Metro North West line) have been used to evaluate OD movement flows. The passenger OD movement data among different stations (or the station-based OD flow) are first estimated using a statistical estimation methodology. The stations-based OD flow data are then translated into region-based OD matrices using the state-of-art method. For more information please see the original metadata file [here](https://resources.aurin.org.au/ckan/documents/ARDC_CSIRO_MobilityAustralia_MetaData.pdf).\n\nHuman mobility data is a key ingredient in various areas and domains of research including epidemiology, policy and administration, criminology, transportation, logistics and supply chains, environmental management and, pollution and contamination. High quality human mobility data provided by telecommunication companies collected from call data records (CDRs) is available at prohibitive cost with restrictive licensing, keeping it out of reach for the majority of research community. On the other hand, there is an abundance of high-quality public data, reporting different aspects of mobility. Examples are the public transport patronage and information about the usage of the Australian road network. These datasets are collected by different organisations and government departments and are presented in various formats. For instance, data may be collected at different spatial (e.g. at state or postcode levels) and temporal scales and be presented in the form of passenger counts or aggregated movement flows. This dataset addresses the general lack of national scale comprehensive human mobility dataset in Australia by transforming available mobility data into a consistent format that is suitable for analysis in a broad range of research areas. Merging the various individual datasets into Australia's first comprehensive, national-scale human mobility data asset drastically improves the quality and coverage of existing datasets.\n\nThe Mobility Australia project received investment [(https://doi.org/10.47486/DP702)](https://doi.org/10.47486/DP702) from the Australian Research Data Commons (ARDC). The ARDC is funded by the National Collaborative Research Infrastructure Strategy (NCRIS).\n\nThe original data tables were structured in a matrix-like format. AURIN employed a methodology to merge diverse datasets into a comprehensive one, categorising based on transportation types (e.g., trains, buses, rails, ferries), years (e.g., 2019, 2020, 2021, etc.), and temporal scales (e.g., weekly, monthly, yearly). Subsequently, AURIN spatially enabled the original data by employing the 2021 edition of the Australian Statistical Geography Standard (ASGS). The flow between origin and destination pairs is visually represented using line geometry.

本数据集基于公共交通（Public Transport, PT）网络连通的新南威尔士州（New South Wales, NSW）统计区域2（Statistical Area 2, SA2）间的起始地-目的地（Origin Destination, OD）客流，估算人类移动性。本研究采用悉尼轨道交通（T1-T9线路）及地铁服务（Metro North West线）连通的新南威尔士州SA2区域，用于评估OD客流。首先通过统计估算方法，对不同站点间的乘客OD出行数据（即基于站点的OD客流）进行估算；随后采用前沿方法，将基于站点的OD客流数据转换为基于区域的OD矩阵。更多详情请参阅原始元数据文件：[点击查看](https://resources.aurin.org.au/ckan/documents/ARDC_CSIRO_MobilityAustralia_MetaData.pdf)

人类移动性数据是流行病学、政策与行政、犯罪学、交通、物流与供应链、环境管理以及污染防控等诸多研究领域的核心要素。由电信公司通过通话记录（Call Data Records, CDRs）采集的高质量人类移动性数据，往往成本高昂且许可限制严苛，绝大多数研究团队难以获取。与之相对，当前存在大量可反映移动性不同维度的高质量公开数据，例如公共交通载客量数据以及澳大利亚道路网络使用情况信息。此类数据集由不同机构与政府部门采集，格式各异。例如，数据可按不同空间尺度（如州级或邮政编码分区）与时间尺度采集，并以乘客计数或聚合客流的形式呈现。本数据集通过将现有移动性数据转换为适用于多领域研究分析的统一格式，解决了澳大利亚缺乏国家级综合人类移动性数据集的普遍痛点。将各类独立数据集整合为澳大利亚首个国家级综合人类移动性数据资产，大幅提升了现有数据集的质量与覆盖范围。

“澳大利亚移动性（Mobility Australia）”项目获得了澳大利亚研究数据共享平台（Australian Research Data Commons, ARDC）的资助[https://doi.org/10.47486/DP702](https://doi.org/10.47486/DP702)。ARDC由国家协同研究基础设施战略（National Collaborative Research Infrastructure Strategy, NCRIS）提供经费支持。

原始数据表采用类矩阵格式进行组织。AURIN通过一套整合方法将多源数据集整合为综合数据集，并按交通类型（如轨道交通、公交、铁路、轮渡）、年份（如2019、2020、2021等）与时间尺度（如周度、月度、年度）进行分类。随后，AURIN采用2021版澳大利亚统计地理标准（Australian Statistical Geography Standard, ASGS）为原始数据赋予空间属性。起始地-目的地对之间的客流通过线几何要素进行可视化呈现。