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tutorials:learning_gis [2025/09/10 19:01] – Taylor Robbinstutorials:learning_gis [2025/09/10 19:28] (current) – Taylor Robbins
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 The is obviously a lot of existing software in and related to maps, so let's go through a few things that I know exist: The is obviously a lot of existing software in and related to maps, so let's go through a few things that I know exist:
 +
 +<graphviz dot center 500x100>
 +digraph {
 +label = "Existing Geospatial Software";
 +GlobalMaps;
 +ExerciseAid;
 +Academic;
 +}
 +</graphviz>
  
 Companies like Google and Apple have a massive investment in collecting information about our planet and providing access to that data in a huge variety of ways. These endeavors represent the sort of primary thing that a computer might help someone do in-relation to geospatial data. Almost everyone has the need for directions and information gathering. The primary goal here is to collect information from various sources (satellites, cars rigged with cameras, etc.) and sort/store/distribute this data to the user in the most convenient way possible. One primary distinguishing factor of this software kind of software is that it deals with largely "timeless information." The roads marked on google maps are likely to stay the same for a long while, and the rate at which they change can be overcome by the rate at which new data is collected (new satellite or streetview images, and internal work done to catalog and transform those inputs into which roads are shown on the map). In some sense all data is "timeless" if it's paired with the time in which the data was collected. You can look at a Google streetview image from years ago and still find the information useful, as long as you know when the data was collected. But most often these tools try to present the data in a timeless manner, trying to promise that the data they are presenting you is up to date as often as possible. Some kinds of data that people might want related to maps is not as "timeless." It changes rapidly, or is labor intensive to collect or process into a format that is useful. Not all timeless information is like this. For example traffic data is very time sensitive, but the method by which a company collects the traffic data is rapid enough that the data can be created and consumed before it becomes irrelevant. One other distinction is that the data is "impersonal", meaning it is useful to many people. A roads existence or name is useful for anyone who may use that road, and although the interest in that road is not equal among everyone, it is useful for enough people that it is worth collecting as part of the dataset and providing access to users. Companies like Google and Apple have a massive investment in collecting information about our planet and providing access to that data in a huge variety of ways. These endeavors represent the sort of primary thing that a computer might help someone do in-relation to geospatial data. Almost everyone has the need for directions and information gathering. The primary goal here is to collect information from various sources (satellites, cars rigged with cameras, etc.) and sort/store/distribute this data to the user in the most convenient way possible. One primary distinguishing factor of this software kind of software is that it deals with largely "timeless information." The roads marked on google maps are likely to stay the same for a long while, and the rate at which they change can be overcome by the rate at which new data is collected (new satellite or streetview images, and internal work done to catalog and transform those inputs into which roads are shown on the map). In some sense all data is "timeless" if it's paired with the time in which the data was collected. You can look at a Google streetview image from years ago and still find the information useful, as long as you know when the data was collected. But most often these tools try to present the data in a timeless manner, trying to promise that the data they are presenting you is up to date as often as possible. Some kinds of data that people might want related to maps is not as "timeless." It changes rapidly, or is labor intensive to collect or process into a format that is useful. Not all timeless information is like this. For example traffic data is very time sensitive, but the method by which a company collects the traffic data is rapid enough that the data can be created and consumed before it becomes irrelevant. One other distinction is that the data is "impersonal", meaning it is useful to many people. A roads existence or name is useful for anyone who may use that road, and although the interest in that road is not equal among everyone, it is useful for enough people that it is worth collecting as part of the dataset and providing access to users.
tutorials/learning_gis.txt · Last modified: by Taylor Robbins

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