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ToxPi*GIS 工具包:使用 ArcGIS 创建、查看和共享地理空间数据的综合可视化。

ToxPi*GIS Toolkit: creating, viewing, and sharing integrative visualizations for geospatial data using ArcGIS.

作者信息

Fleming Jonathon, Marvel Skylar W, Supak Stacy, Motsinger-Reif Alison A, Reif David M

机构信息

Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA.

Center for Geospatial Analytics, College of Natural Resources, North Carolina State University, Raleigh, NC, USA.

出版信息

J Expo Sci Environ Epidemiol. 2022 Nov;32(6):900-907. doi: 10.1038/s41370-022-00433-w. Epub 2022 Apr 26.

Abstract

BACKGROUND

Presenting a comprehensive picture of geographic data comprising multiple factors is an inherently integrative undertaking. Visualizing such data in an interactive form is essential for public sharing and geographic information systems (GIS) analysis. The Toxicological Prioritization Index (ToxPi) framework offers a visual analytic integrating data that is compatible with geographic data. ArcGIS is a predominant geospatial software available for presenting and communicating geographic data, yet to our knowledge there is no methodology for integrating ToxPi profiles into ArcGIS maps.

OBJECTIVE

We introduce an actively developed suite of software, the ToxPi*GIS Toolkit, for creating, viewing, sharing, and analyzing interactive ToxPi profiles in ArcGIS to allow for new GIS analysis and an avenue for providing geospatial results to the public.

METHODS

The ToxPi*GIS Toolkit is a collection of methods for creating interactive feature layers that contain ToxPi profiles. It currently includes an ArcGIS Toolbox (ToxPiToolbox.tbx) for drawing location-specific ToxPi profiles in a single feature layer, a collection of modular Python scripts that create predesigned layer files containing ToxPi feature layers from the command line, and a collection of Python routines for useful data manipulation and preprocessing. We present workflows documenting ToxPi feature layer creation, sharing, and embedding for both novice and advanced users looking for additional customizability.

RESULTS

Map visualizations created with the ToxPi*GIS Toolkit can be made freely available on public URLs, allowing users without ArcGIS Pro access or expertise to view and interact with them. Novice users with ArcGIS Pro access can create de novo custom maps, and advanced users can exploit additional customization options. The ArcGIS Toolbox provides a simple means for generating ToxPi feature layers. We illustrate its usage with current COVID-19 data to compare drivers of pandemic vulnerability in counties across the United States.

SIGNIFICANCE

The integration of ToxPi profiles with ArcGIS provides new avenues for geospatial analysis, visualization, and public sharing of multi-factor data. This allows for comparison of data across a region, which can support decisions that help address issues such as disease prevention, environmental health, natural disaster prevention, chemical risk, and many others. Development of new features, which will advance the interests of the scientific community in many fields, is ongoing for the ToxPi*GIS Toolkit, which can be accessed from www.toxpi.org .

摘要

背景

呈现包含多个因素的地理数据全貌本质上是一项综合性工作。以交互形式可视化此类数据对于公众共享和地理信息系统(GIS)分析至关重要。毒理学优先级指数(ToxPi)框架提供了一种与地理数据兼容的集成数据的可视化分析方法。ArcGIS是一款用于呈现和交流地理数据的主流地理空间软件,但据我们所知,尚无将ToxPi剖面图集成到ArcGIS地图中的方法。

目的

我们介绍一套正在积极开发的软件,即ToxPi*GIS工具包,用于在ArcGIS中创建、查看、共享和分析交互式ToxPi剖面图,以实现新的GIS分析,并为向公众提供地理空间结果提供途径。

方法

ToxPi*GIS工具包是一组用于创建包含ToxPi剖面图的交互式要素图层的方法。它目前包括一个用于在单个要素图层中绘制特定位置ToxPi剖面图的ArcGIS工具箱(ToxPiToolbox.tbx)、一组模块化Python脚本,可从命令行创建包含ToxPi要素图层的预设计图层文件,以及一组用于有用的数据操作和预处理的Python例程。我们展示了工作流程,记录了ToxPi要素图层的创建、共享和嵌入,适用于寻求更多可定制性的新手和高级用户。

结果

使用ToxPi*GIS工具包创建的地图可视化可以在公共URL上免费提供,使没有ArcGIS Pro访问权限或专业知识的用户也能查看并与之交互。具有ArcGIS Pro访问权限的新手用户可以创建全新的自定义地图,高级用户可以利用更多的定制选项。ArcGIS工具箱提供了生成ToxPi要素图层的简单方法。我们用当前的COVID-19数据说明了其用法,以比较美国各县大流行脆弱性的驱动因素。

意义

将ToxPi剖面图与ArcGIS集成,为多因素数据的地理空间分析、可视化和公众共享提供了新途径。这使得能够跨区域比较数据,从而支持有助于解决疾病预防、环境卫生、自然灾害预防、化学风险等诸多问题的决策。ToxPi*GIS工具包正在持续开发新功能,这将推动许多领域的科学界利益,可从www.toxpi.org访问该工具包。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80ed/9731901/6b9714524428/41370_2022_433_Fig1_HTML.jpg

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