Facilities and enforcement case searches can both easily be limited by geography (EPA region, city, state, zip code, county, proximity to national border, and watershed). The tool also automatically produces maps that allow users to see the distribution of facilities across space.
This database uses a broad variety of data. Most of the data is collected by the EPA itself. Users are able to search for facilities regulated under the following systems:
When looking at individual facilities, the database provides detailed facility reports, enforcement case reports (civil and criminal), air pollutant reports, effluent charts, pollutant loading reports, effluent limit exceedances reports, CWA program area reports, permit limits reports, and other facility documents as available. The database provides easy ways to download and map the data. The database also allows users to narrow facilities searches using demographic data from EJScreen (also maintained by the EPA), the U.S. Census, and tribal land data.
Users can also look for information on federal administrative and judicial enforcement actions through an enforcement case search.
ECHO is maintained by the U.S. Environmental Protection Agency (EPA). Its purpose is to provide comprehensive information about all of the facilities regulated by the EPA. It is connected directly to the EPA's internal enforcement record system, so information is currrent.
The Enforcement and Compliance History Online (ECHO) Database, maintained by the Environmental Protection Agency (EPA).
Environmental Protection Agency (EPA). Enforcement and Compliance History Online (ECHO) Database. 2022. Available online: https://echo.epa.gov/ (accessed on 17 March 2022).
The authors review literature on the datafication of health, which they identify as the way through which health has been quantified on a number of different scales and registers. They focus primarily on the datafication of health in clinical health care and self-care practices, rather than medical research and public health infrastructures. From this literature, they identify three key themes: datafied power (the ways through which data permeates and exerts power over forms of life), living with data (focused on datafication as an intimate form of surveillance, and a technology of the self), and data-human mediations (which emphasizes the nonhuman elements mediating datafication dynamics and experiences—such as algorithms, data infrastructure and data itself).
In examining literature on datafied power, the authors acknowledge a lack of scholarship on understanding data and datafication in terms agency, rather than simply power and domination. For instance, data is sometimes mobilized in “creative and even pioneering ways (Rapp 2016)” (265).
They describe literature on “living with data” as increasingly focus examining the social, narrative, and affective dimensions of data practices and experiences (e.g. work on the “Quantified Self,” a group seeking self-knowledge through numbers – a form of relationality that might be described as datasociality). Some scholars have argued that data can render “‘feelings and problems more tangible and comparable” (Sharon & Zandbergen 2016, p. 11)” (267). Some have also acknowledged as well a “curious resonance between the vision of empowered, resisting individuals that many ethnographers of self-tracking celebrate, and the rhetoric of consumer empowerment found in discourses of digital health (Schull 2017, Sharon 2017)” (267).
The literature on data-human mediations emphasizes the agency, liveliness and/or performativity of nonhuman elements—essentially, how they structure and shape the possibilities for action. For instance: “as social expectations of normality and health become embedded in tracking devices’ target numbers, presentation of scores, and gamified incentives (Depper & Howe 2017, Whitson 2013), a “numerical ontology” comes to suffuse everyday practices and “the ways in which people relate to their own bodies” (Oxlund 2012, p. 53; see also Jethani 2015, p. 40)” (269). Perspectives and action can be enabled or disabled by wide variety of factors: the design and performativity of data technology software (user interface, operational and analytical algorithms), hardware (devices, sensors), data itself (as illustrated in different ways), and data infrastructures (labs, data centers, serve and cloud storage, and networks that organize how data is stored and circulated). An analytically constructive focus in this literature has emerged by applying the concept of “assemblage” as a way of tracing how data moves: “where it flows, where it finds impasses, how algorithms act on it along the way” (270).
Lastly, the authors identify scholarship on “data activism” as an emerging focus on exploring how data technology capacities might be employed to promote social justice, collective action, and political participation, as well as to challenged dominant norms and ideologies: “Individual self-tracking data, for instance, can have social and political potential when it is pooled to identify health inequalities, collective environmental exposure, or disparities in quality of life (Gabrys 2014).” (271)
The system was built to serve organizations and individuals with humanitarian goals. The system gathers data from report, reviews, and users and compiles it into comprehensible information to help inform decision-making for humanitarian concerns. Portions of the app also focus on education and technical support for field researchers looking to collect large quantities of data.
The system is primarily used by researchers, scholars, and organizations with humanitarian interests. The app also has functions which would attract users that are beginning research and do not have established connections within the field as the app provides a support system.
The development of Twine was funded primarily through donations from individual investors interested in the data sharing aspects of the software and humanitarian aid organizations who benefit from the accessible data.
Twine provides information and software to set up compatible data collection systems that pool information into the larger system, which the app then makes available to its users. The system also includes a publishing and collaboration aspect which allows groups of people from all over the globe to access the same data and report on the findings together.
Data visualization is primarily determined by the type of data being gathered and the system for data collection being used. The app integrates data in order to both make large quantities of data easily viewable and understood, as well as compare studies with existing data stored on the system.