加拿大各地衡量社区资源的最佳做法:编码分类比较

IF 1.4 4区 社会学 Q2 GEOGRAPHY
Marisa Young, Sean Leipe, Diana Singh
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引用次数: 0

摘要

社会科学家、地理学家、犯罪学家和健康学家的任务往往是寻找数据,以最好地捕捉 "社区环境 "对个人结果的影响,包括居住服务、物质资源和社会机构。数字地图技术公司 (DMTI) Spatial 是加拿大提供此类数据的一个渠道,该公司提供了一个包含 100 多万个商业和娱乐景点的全国性数据库。该数据库通过 CanMap Streetfiles 生成,其中包括每个点精确位置的地理编码。研究人员可从其大学数据图书馆和 Esri 加拿大公司获得这些数据,但主要面向私营部门和政府市场。尽管如此,本文的目的是鼓励研究人员访问这一丰富但利用率不高的数据源。DMTI 空间数据库中的每项服务、业务或资源都使用标准产业分类代码和北美产业分类系统代码分配到相应的类别中。但是,目前还不清楚哪种编码标准更可靠。我们将概述我们对 DMTI Spatial 数据的审查情况,并就如何利用这一宝贵资源开展中层住宅标记的未来研究提出建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Best practices for measuring community resources across Canada: A comparison of coding classifications

Social scientists, geographers, criminologists, and health scientists are often tasked with finding data to best capture the impact of “community context” on individual outcomes, including residential services, physical resources, and social institutions. One outlet for such data in Canada is Digital Map Technologies Inc. (DMTI) Spatial, which offers a national repository of over one million businesses and recreational points of interest. The database is generated through CanMap Streetfiles, which includes geocodes of each point's precise location. These data are available to researchers from their university data library and Esri Canada, but primarily available to private sector and government markets. That said, the goal of the current paper is to encourage researchers to access this rich yet under-utilized data source. Each service, business, or resource in the DMTI Spatial database is assigned to a respective category using Standard Industrial Classification codes and North American Industrial Classification System codes. It is not clear, however, which is the more reliable coding criteria. We provide an overview of our review of DMTI Spatial data and take-away suggestions for using this valuable resource for future research on meso-level residential markers.

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来源期刊
CiteScore
4.40
自引率
11.10%
发文量
76
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