This sort of imprecision and inaccuracies are always expected in the bulk of data we source, however, we have managed to teach our algorithm – based on machine learning – to identify & fix the sources of these errors. Some of these sources can include:
Scalability becomes particularly complex as well as important because most geospatial maps are drawn on a Fractional Scale. On this type of scale, the amount of reduction between the real world and its graphic representation determines how well the GIS map can be enlarged or shrunk, if needed. Examining this kind of scale helps us understand the problem and its effects which may vary by geographical location. We measure fractional scales by the ratio of the distance between two points on a map and the distance between those points on the ground. For example, 1 unit on the map could be 10,000 cm on the earth so the fractional scale will represent 1:10,000.
Most fractional scales are demonstrated by a display scale which includes details of a particular place as well as the size and placement of texts and symbols. The problem with this kind of display is that it can quickly become overcrowded.
This type of scale is notorious for its inaccuracies and imprecisions. The major issues of fractional scale include:
- Omitted features that are there on earth but are not represented on the map
- Representing features that do not exist on earth
- Incorrect classification of attributes
- Inaccurate location, such as the spot represented on the map might vary from the actual location on earth
- As these problems are usually expected, data experts always ensure that a firm statement of location accuracy is mentioned in a dataset. As mentioned above, 30% of polygons are 80% accurate is a statement of location accuracy. Data experts might also include statistics of uncertainty and the method of collecting the information.
When a bulk of data comes at once from various sources, data experts always expect an immense amount of error in formatting. Such as two different sources of data can be duplicated but since they are presented differently the algorithm might read them as two separate pieces of information. If two sources provide the same phone numbers and one of those phone numbers includes an international code then it becomes necessary to first of all set a proper format. Data experts consider this type of edge case can lead to possible errors while creating a dataset.
One of the most important and common issues that data experts face is the process of classifying a huge amount of data. While classifying the data, data experts follow some standardized methods such as NAICS (North American Industry Classification System) which are established to collect, analyze, and publish statistical data. However, companies often require data that is need-based as well as something they can rely on for their strategies. It compels data experts to ask questions like, “What data or information does the company require the most?”
Taxonomy errors occur when the data is not classified according to the company’s needs. Data experts pay close attention to taxonomy to avoid any kind of classification error that can potentially lead the dataset to be useless.
The complexity of the issues and aiming for perfection are the two things that make geospatial data analysis so demanding. Data experts like the ones at Echo Analytics take extra measures to ensure that these issues are wiped out so that the client receives accurate and precise data.
We also provide our clients with customized datasets that are classified and labeled so that they can get the information that they can rely on for their business.