Mobility Australia - Queensland Weekly Ferry 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（QLD）内由公共交通（PT）网络连接的二级统计区（SA2）之间的起点-终点（OD）移动流量来估算人类流动性。昆士兰州内由公交、火车、有轨电车和渡轮连接的SA2区域被用于评估OD移动流量。

不同站点间的乘客OD移动数据（或称基于站点的OD流量）首先通过统计估算方法得出。随后，基于站点的OD流量数据采用最先进的方法转换为基于区域的OD矩阵。更多信息请参见原始元数据文件[链接](https://resources.aurin.org.au/ckan/documents/ARDC_CSIRO_MobilityAustralia_MetaData.pdf)。

人类流动性数据是流行病学、政策与管理、犯罪学、交通运输、物流与供应链、环境管理以及污染与污染物等多个研究领域的关键要素。电信公司基于通话数据记录（CDRs）提供的高质量人类流动性数据成本高昂且许可限制严格，多数研究机构难以获取。另一方面，存在大量高质量公共数据，涵盖流动性的不同方面，例如公共交通客流量及澳大利亚道路网络使用信息。这些数据集由不同组织和政府部门收集，格式各异。例如，数据可能在不同空间尺度（如州或邮政编码级别）和时间尺度上收集，并以乘客数量或聚合移动流量的形式呈现。本数据集通过将现有流动性数据转换为适用于广泛研究领域分析的统一格式，解决了澳大利亚缺乏全国性综合人类流动性数据集的问题。将各类独立数据集整合为澳大利亚首个综合国家级人类流动性数据资产，大幅提升了现有数据集的质量和覆盖范围。

澳大利亚流动性项目（Mobility Australia）获得了澳大利亚研究数据共享平台（ARDC）的资助[(https://doi.org/10.47486/DP702)](https://doi.org/10.47486/DP702)。ARDC由国家协作研究基础设施战略（NCRIS）提供资金支持。

原始数据表采用类矩阵格式结构。AURIN采用一种方法将多样化数据集整合为综合数据集，并基于交通类型（如火车、公交、铁路、渡轮）、年份（如2019、2020、2021等）和时间尺度（如周、月、年）进行分类。随后，AURIN通过采用2021版澳大利亚统计地理标准（ASGS）为原始数据赋予空间属性。起点-终点对之间的流量通过线几何图形进行可视化呈现。