To summarize the entire map making process in just paragraphs is impossible. Communicating map information is also a complex process. This section is meant to provide a theoretical approach to the process of making maps and how map information is processed and communicated to the map reader and the role each stakeholder plays. A great deal of research was performed on these topics by research cartographers in the 1960s and 1970s; their communication models are still applicable today.
The ideal map is supposed to be a representation of everything that exists in the real world. But this is impossible simply because in order to do so, the map will have to be almost as big as the real world. By representing the real world in a reduced scale, certain amounts of information must be left out. The following diagram is an attempt to synthesize the major cartographic processes and map reading processes that take place.
To make a map, a cartographer must first define some parameters. The first map-making step is to define the purpose of the map. Once this is done, the next step is to gather or collect information or data about what we wish to map at an appropriate scale. Are there existing data available from government agencies? If no data are available, how difficult is it to specifically gather the relevant data for the map? The general methods of data collection include recording GPS locational data, using aerial photography and satellite images, using drones for low altitude mapping, conducting ground surveys, taking a census, interviewing subjects, or any other data collection method that involves a geographical component.
Once data have been gathered, there is a set domain of information that is ready for the mapping process. At this point, the cartographer needs to make several important decisions. The amount of data collected can be massive or inadequate. For massive datasets, the main question to consider is whether the scale and other parameters of the map can accommodate such a large amount of data. If not, the data must be generalized. For inadequate datasets, the concern here is whether the limited amount of information can truly represent a spatial phenomenon meaningfully. Would there still be uncertainties in the dataset that may affect the mapping processes? Is the source of the data reliable? These are just some important decisions for the cartographer to make because they can ultimately affect the quality and integrity of the map. There are still many other decisions, but it is apparent that the map making process is very complicated.
<drawing> A functional map-making and map information communication model
Would the average map reader be able to spot these data quality questions? Generally speaking, spatial data collected by government agencies tend to be meticulous and truthful, but still not without errors. Data collected by researchers may have to depend on the researchers’ efforts, their funding, and other parameters. The number of data points collected may indicate the quality of the dataset. The U.S. Bureau of the Census and equivalent census bureaus or central statistical offices of other governments tend to acquire complete information on their complete respective populations. Surveys of other topics may only be based on sampling methods rather than a complete survey: for example, taking a 10% or 20% sample of the entire population and extrapolating (taking a small amount of data and projecting them or making inferences proportionally to represent the entire population) these samples to represent 100%. Unless the map legend specifically indicates how a dataset was derived, the map reader generally may not know. Today, in an environment of digital reality and digital piracy, the act of data mining (taking someone else’s dataset —ith or without permission—o incorporate into one’s own dataset) can be a tricky, if not dangerous, maneuver, however common.
It is clear that the cartographer bears a great deal of responsibility to make careful selections of data that are available for the mapping process. Selecting the proper data may also mean that some less relevant data may be purposely omitted. Data must also be classified into meaningful categories so that they will become manageable or can be presented in an easily comprehensible way.
Datasets can also be complicated. Depending on whether the intended scale of the map can accommodate the presentation of all available data, map data normally must be generalized and simplified. For instance, many maps may only show major roads in an urban setting to provide an adequate vision of the layout of the urban area. That does not mean that other smaller roads, streets, or cul-de-sacs are not present; these smaller and less important features have been simplified so that they will not visually clutter the map and subsequently interfere with the map reader’s visualization process. Generalization of map data can be performed in many different ways. The map reader has to be aware that generalization is a common occurrence on maps. The degree of generalization dictates the accuracy of the spatial phenomenon being displayed, as intended by the cartographer.
Finally, all map information must be symbolized into graphic forms that can be displayed on the map. The application of the proper map symbols would also contribute to the ease of visualization by the map reader. Some symbols are more intuitively interpreted than others. Other symbols are so small that if truly mapped to scale, the cartographer will exercise some judgment in exaggerating the size of the symbol so that it can at least be visible to the map reader. Using the wrong symbol or visually nonconspicuous symbols will lead to the wrong mental image of the map as interpreted by the map reader.
Thus, the cartographer is really entrusted with the generalization processes in the simplification, selection, classification, symbolization, and exaggeration of map data or information before the final map can be properly designed with the optimal graphic settings and produced to achieve its intended purpose. Once the map is finalized and produced, it is ready to be disseminated, whether by being published in sheet, book, or atlas formats or digitally for display and analysis on the Internet or any other display screens. Realistically, the cartographer is seldom available for consultation by the map reader. The only communication channel that links the conceptual framework and purpose of the map to the map reader’s understanding of the map elements is the set of symbols, legend, title, and any other peripheral information that are skillfully designed and displayed on the map. The legend is especially important to the translation of the mapped information to a mental image. It would then be up to the map reader’s sole efforts to visualize, analyze, and interpret the map that determines ultimately how much of the cartographer’s intended map message is to be absorbed and understood by the map reader. Different map readers have varying ability in conducting these map-reading tasks. Today, with the interactive nature of the Internet, maps published on the Internet may finally provide a blogging mechanism for map readers to ask the map publisher questions and to provide feedback to the cartographer. To learn more about the selection, classification, generalization, and symbolization of map data, please read Chapter 10 of Map Use (Kimerling, 8th edition).
To lead the map reader to understand the intent of a map is an ultimate goal of the cartographer. However, there is an additional higher level intellectual process that a map reader can perform: making inductions and inferences from the elements presented on the map. To make an induction or inference is to mentally relate one thing to another, one spatial entity to another, or one spatial entity to the same spatial entity on a different map (in the case of an atlas) or for different time frames. These can be performed by techniques such as thinking spatially, making spatial associations, visualizing the area mapped, and deriving new spatial knowledge from the map. Two seemingly unrelated maps from the National Atlas of Korea I (p.82 and p.93) illustrate this idea of spatial association.
<drawing> Annual Average Population Change and Backward Region
<drawing> New Industrial Production Bases
<drawing> New Industrial Production Bases with Backward Region Boundary
Superimposed
The map on the left appears on Page 82 of the National Atlas of Korea I with the title “Annual Average Population Change and Backward Region.” The map in the middle appears on Page 93 of the same Atlas, with the title “New Industrial Production Bases.” From reading the titles, these two maps appear to be totally unrelated. The map on the right illustrates the technique of spatial association (taking one spatial entity and associating it with other spatial phenomena). Taking the “Backward Region” boundary from the map on the left and superimposing it over the map in the middle results in the map on the right. Thus, using the spatial entity (Backward Region) from one map and associating it with spatial phenomena on another map quickly indicates that there are only four dot locations which are defined as “new industrial production bases.” The majority of the dots lie outside the “Backward Region.” With this spatial association, a map reader can immediately raise some questions: Why are there so few industrial bases (only 4) in the Backward Region? Did the loss of population in the Backward Region contribute to undesirable conditions for the government planner to place new industrial bases in this region? What geographic factors contribute to the loss of population and less than ideal conditions to develop industrial bases? The list of intellectual questions can be many. Could there be answers to these questions? Could these answers lead to a better understanding of the conditions in the region? More research needs to be done, but at least curiosity has been piqued, which is a major element of all map reading.
Raising these questions typifies the process of spatial thinking. In addition to raising these questions, the next step in a scientific inquiry is to generate a hypothesis. Hypotheses that can be generated and proven will inherently add to a knowledge base. Increasing the number of spatial thinking activities performed will enhance the chances of discovering possible hypotheses.
