Expanding the Data Stream with GIS

Expanding the Data Stream with GIS

Expanding the Data Stream with GIS
GIS is an essential component in many decision and management processes. A well-structured GIS provides invaluable tools to visualize, analyze and query geospatial data and associated information about features and objects in both the natural and built environments. Because a GIS database can contain information on a wide variety of features and terrain, it is commonly built and maintained using information produced by a broad range of input and data sources.

As applications for GIS data expand, so does the demand for new and efficient ways to collect and deliver quality, actionable spatial data from the field. Satisfying the seemingly insatiable demand for data doesn’t always involve traditional GIS field technicians. Certain types of geospatial data can be produced by the general public. And in some cases, data collection doesn’t involve humans at all.

Today’s widely available options for connectivity and Internet-based communications are enabling new approaches to collecting and using GIS information. We can divide the techniques into three broad classifications: crew sourced, crowd sourced and automated acquisition.

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Crew-sourced GIS data follows the traditional path of using personnel who are trained and equipped for GIS data acquisition. Technicians typically possess specialized knowledge in applications for which they are gathering data. For example, a person working to collect wetlands data may be a botanist, environmental scientist or marine biologist. Similarly, people collecting information about roads and highways may have training in civil engineering, transportation technology or related disciplines.

Although field equipment for crew-sourced GIS data collection can include consumer-grade devices such as smart phones or tablets, many organizations prefer to use dedicated equipment. These solutions can operate in difficult environments and provide higher reliability and performance in the commercial and industrial arenas. In addition, solutions such as the Trimble® Geo 7 series handhelds can produce GNSS positions with higher precision than standard smartphones and tablets.

By contrast, crowd-sourced data collection is conducted by the general public and requires little knowledge of assets or technologies. In fact, most people participating in this activity don’t know that they are capturing geospatial data. While the technique can leverage large numbers of potential data collectors, it faces challenges in assessing incoming data to develop reliable, actionable information.

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In looking at crowd-sourced data collection, we can identify two common approaches to gathering and using data. Both combine geolocations with user-reported attributes or situations. One method uses apps that enable citizens to report problems or concerns about an item of interest. For example, a person could report a failed traffic signal, broken concrete sidewalk or damage to a road sign. The report, which often includes a GNSS location of the problem, goes to the municipal department responsible for the issue. The department reviews the report and schedules any needed repair or maintenance activities. This “managed crowd-sourced solution” may also include feedback to the citizen upon completion, informing them that their report was heard and acted upon.

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