EAGER: Prototyping an Urban Data Cyberinfrastructure for Computational Social Sciences

城市是文明的熔炉，全球城市化加速带来了与交通、粮食生产和分配、人类健康和福祉、教育、社会政策和服务以及水和能源管理等领域的密度和规模有关的挑战和机遇。寻求了解人类，社会和经济，以帮助发展有效的教育或公共政策科学家，因此城市官员，传统上限于定性研究或使用稀疏，往往陈旧的数据源。开放数据运动正在提供越来越丰富的城市数据，但用于提供这些数据的网络基础设施技术和工具主要是为了支持对单个数据集的分析，而不是探索许多数据集之间的关系。因此，来自社会学、经济学、行为科学、教育学、工程学、运筹学和其他学科的城市科学家缺乏充分利用城市数据进行研究的工具和基础设施。因此，这些研究人员提出的问题受到他们手头数据的限制。两种新的网络基础设施能力有可能释放这些数据源，它们都利用了大多数已发布的城市数据集共享位置和时间属性的事实。第一种是允许科学家从多个独立的数据源收集特定地理位置点（纬度/经度）、城市单元（街道段、人口普查区、街区）或区域（多边形）的数据。第二种方法是选择一个时间窗口，并使用一个共同的采样间隔对选定的数据源进行归一化，将它们合并成一个复合结构，用于计算和统计分析。综合起来，这些能力将使科学家能够研究城市地区，在特定的时间段内，不同的，相关的数据表示为一个时间序列的矢量。我们建议与城市科学家和城市官员合作开发，最初来自芝加哥，最终来自纽约市，这些功能的概念验证。原型将从开放的数据门户网站获取数据，允许研究人员指定位置，时间窗口，采样周期和数据集列表。系统将提供一个矩阵，每个时间样本一行，列代表每个数据集。通过将城市数据合并和转换成矩阵，我们将使城市科学家能够应用数学和计算工具来了解城市挑战，从青年暴力和犯罪到毕业率，再到就业和经济衰退与振兴。

Cities are the crucibles of civilization, and accelerating global urbanization raises challenges and opportunities related to density and scale in areas including transportation; food production and distribution; human health and wellbeing; education; social policy and services; and management of water and energy. Seeking to understand the human, social, and economic to help develop effective education or public policy scientists, and thus city officials, have traditionally been limited to qualitative studies or to using sparse, often stale data sources. The open data movement is making an increasingly rich set of urban data available, but the cyber infrastructure technologies and tools used to make this data available were designed primarily to support the analysis of individual data sets rather than exploring relationships among many data sets. Consequently, urban scientists from sociology, economics, behavioral sciences, education, engineering, operations research, and other disciplines lack the tools and infrastructure to fully harness urban data for their research. The questions these researchers ask are therefore constrained by the data they have in hand. Two new cyber infrastructure capabilities have potential to unleash these data sources, both exploiting the fact that most of the published urban data sets share the attributes of location and time. The first is to allow a scientist to assemble data from multiple, independent, data sources for a specific geographical location point (latitude/longitude), city unit (street segment, census tract, block), or area (polygon). The second is to select a window of time and to normalize the selected data sources using a common sampling interval, merging them into a composite structure for computational and statistical analysis. Taken together, these capabilities will allow a scientist to study urban areas, over specific time periods, with varied, relevant data represented as a time series of vectors. We propose to develop, in partnership with urban scientists and City officials initially from Chicago and eventually from New York City, a proof-of-concept with these capabilities.The prototype will draw data from open data portals, allowing a researcher to specify a location, a window of time, a sampling period, and a list of data sets. The system will provide a matrix with one row per time sample and columns representing each data set. By merging and transforming urban data into matrices we will enable urban scientists to apply the tools of mathematics and computation to understand urban challenges ranging from youth violence and crime to graduate rates to employment and economic decline and revitalization.