Mobility Australia - Queensland Weekly Bus Origin-Destination Flow (SA2) 2022

This dataset estimates human mobility through origin destination (OD) movement flow among the Statistical Area 2 (SA2) regions in Queensland (QLD), connected by public transport (PT) networks. The SA2 regions of Queensland connected by buses, trains, trams and ferries 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.本数据集通过昆士兰州（Queensland, QLD）内统计区域2（Statistical Area 2, SA2）间的起止点（origin destination, OD）出行流，估算人类移动模式，这些区域由公共交通（public transport, PT）网络连接。研究采用了由公交、火车、有轨电车及渡轮连接的昆士兰州SA2区域，用于评估起止点出行流。首先，基于站点的起止点出行流（即站点级OD流）的乘客数据，先通过统计估算方法完成测算。随后，借助当前前沿方法，将站点级OD流数据转换为区域级OD出行矩阵。更多详细信息请参阅原始元数据文件[此处](https://resources.aurin.org.au/ckan/documents/ARDC_CSIRO_MobilityAustralia_MetaData.pdf)。 人类移动数据是流行病学、政策与行政管理、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。此前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数据，往往许可限制严格且成本高昂，令多数研究团队难以获取。与之相对，目前存在大量高质量的公开移动相关数据，涵盖出行的多个维度，例如澳大利亚公共交通客流量数据以及公路网络使用情况信息。这类数据集由不同机构与政府部门采集，格式各异：数据的空间尺度（如州级或邮编区划级）与时间尺度（如周、月、年）各不相同，且以乘客计数或聚合出行流的形式呈现。本数据集通过将现有移动数据转换为适用于多领域研究的统一格式，解决了澳大利亚全国范围内综合级人类移动数据集普遍缺失的问题。将各类分散数据集整合为澳大利亚首个全国范围的综合级人类移动数据资产，可大幅提升现有数据集的质量与覆盖范围。 澳大利亚移动项目（Mobility Australia）获得了澳大利亚研究数据通用设施（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）提供资金支持。 原始数据表采用类矩阵格式进行组织。AURIN团队采用相关方法将分散的数据集整合为综合数据集，并按交通类型（如火车、公交、轨道交通、渡轮）、年份（如2019、2020、2021年等）及时间尺度（如周度、月度、年度）进行分类。随后，团队借助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。起止点对之间的出行流可通过线几何要素进行可视化展示。