How to create a heat map with the Geospatial Information Authority of Japan's open data

Table of Contents

• Why the Geospatial Information Authority of Japan's open data is well suited for creating heat maps

• Types of Geospatial Information Authority of Japan (GSI) open data suitable for creating heatmaps

• Basic steps for creating a heat map using Geospatial Information Authority of Japan (GSI) open data

• Common points of confusion in practical data processing

• How to Read Heatmaps and When to Use Them

• Pitfalls and countermeasures when creating heat maps

• Operational approach to linking heatmaps to on-site inspections

• Summary

Why the Geospatial Information Authority of Japan's Open Data Is Well-Suited for Creating Heat Maps

A heat map is a visualization technique for intuitively capturing differences in values at individual locations and biases in their distribution through variations in color intensity. Because it makes disparities, concentrations, boundary changes, and continuous trends—hard to see in raw tables—easy to understand at a glance, it has become a very practical and widely used form of representation in real-world work. Especially when combined with geographic information, a heat map enables spatial understanding of where things are happening rather than just aggregated results, and is therefore utilized across a wide range of fields such as surveys, planning, maintenance, inspection, disaster response, and sales area analysis.

One reason the Geospatial Information Authority of Japan's open data attracts attention is that, first and foremost, it is reliable as a foundation for positional information. With heat maps, if the underlying coordinates or terrain information are ambiguous, it’s easy to draw the wrong conclusions even if the visualization looks plausible. If the background map is misaligned or understanding of elevation and topography is insufficient, the meaning of areas with intense colors can be misinterpreted. The geospatial information published by the Geospatial Information Authority of Japan is easy to use as a basis for map alignment and terrain understanding, making it well suited as background or supplementary information for heat maps.

Also, the open data from the Geospatial Information Authority of Japan is a major strength because it can be easily combined with ancillary information needed for spatial analysis, such as terrain, elevation, base maps, aerial photographs, place names, and administrative boundary delineations. Heat maps can reveal trends on their own, but in practice interpretations such as “why is it high there?”, “is that distribution influenced by topography or by land use?”, and “how does it relate to the surroundings?” are important. In such cases, having the Geospatial Information Authority of Japan’s data as a background makes it easier to connect the color distribution with on-site conditions.

Furthermore, the Geospatial Information Authority of Japan’s open data has the advantage that practitioners can start using it without having to prepare a map base from scratch. What really takes time when creating a heat map is not the coloring itself, but preprocessing tasks such as preparing the background map, organizing coordinates, clipping the area, checking consistency with terrain, and adjusting aggregation units. By using the Geospatial Information Authority of Japan’s open data, some of this preprocessing can be standardized and turned into reproducible procedures. Because it makes it easier to work on the same base even when personnel change, it also helps prevent the work from becoming person-dependent.

Also, don’t forget that a heat map is not a “diagram meant to look flashy” but a “diagram that supports decision-making.” The point of using the Geospatial Information Authority of Japan’s open data is not simply to improve the map’s appearance, but to produce visualizations that can withstand accountability. In meeting materials and reports, you will be asked which area was targeted, which base map was overlaid, and under what terrain conditions the distribution was shown. In such cases, heat maps made with the Geospatial Information Authority of Japan’s open data make it easier to explain the background conditions and are more readily accepted in practice.

Many readers who search for "heat map Geospatial Information Authority of Japan" are likely not simply trying to create a colored diagram; rather, they want to view distributions in a form that can be used for their work, using the Geospatial Information Authority of Japan's publicly available information as a foundation. That is why what matters is less the appearance of a heat map than understanding which data to use, what it represents, how to interpret it, and how to connect it to on-the-ground decision-making. Once you understand this, the Geospatial Information Authority of Japan's open data becomes apparent not as mere background maps but as a valuable foundation for analysis.

Types of Geospatial Information Authority of Japan (GSI) Open Data That Are Easy to Use for Heatmap Creation

When creating a heat map using the Geospatial Information Authority of Japan's open data, the first thing to understand is that the colors of a heat map are not automatically generated from the Authority's data itself. In many cases, the numerical values that form the basis of the colors come from the user's own data, such as observation points, inspection records, survey locations, traffic volume, counts of anomalies, distributions of inquiries, and work histories. The Geospatial Information Authority of Japan's open data is used as a foundation to correctly place those values on the map and to interpret their meaning. Understanding this division of roles clarifies which data should be used.

First, the basic element is the topographic maps or map images used as the map background. If a heat map shows only color gradations, it can be difficult to tell what area is being indicated. Having a background that shows the positional relationships of roads, rivers, place names, and map features connects the color distribution to on-the-ground context. Depending on the size of the target area and the intended use, switching between backgrounds suited for wide-area overview and those suited for detailed inspection of the surroundings can greatly change how easy the heat map is to understand.

Next in importance are data on elevation and terrain. In many practical applications, spatial distribution biases are strongly influenced by topography. For example, the intensity gradients on heat maps are closely related to terrain conditions—where water tends to accumulate, areas difficult to traverse, poor lines of sight, changes along slopes, concentration along valleys, and reductions on ridgelines. By overlaying elevation and terrain information in the background, what used to be mere clusters of color can be interpreted as terrain-derived patterns. This is particularly useful for disaster response, maintenance management, patrol route planning, and preliminary assessments in civil engineering and surveying.

Furthermore, having publicly available photographic information deepens the interpretation of heat maps. This is because, while checking surface conditions that cannot be determined from maps or elevation alone, you can supplement what kinds of features or land uses are present in the darker-colored areas. For example, even with the same high-density distribution, the meaning changes depending on whether it is an urban area, a developed site, a waterfront, or bare ground. Heat maps only show variations in numerical intensity, so photographic information improves the accuracy of understanding current conditions.

Boundary information close to base maps and reference location data are also useful. When reading heat maps, it is often necessary to compare distributions within divisions such as administrative areas, survey plots, facility boundaries, work sections, and management areas. A heat map itself is a continuous color representation, but decision-making is frequently carried out in discrete units. To determine which areas show higher intensity, whether trends change across boundaries, or whether there are biases when viewed by management unit, underlying locational information is indispensable. The Geospatial Information Authority of Japan's open data serves as useful supplementary information to help interpret these divisions.

Also, data and reference frameworks related to handling coordinate systems must not be overlooked. While heat maps are easy to share as visual diagrams, when on-site work or cross-referencing with other materials is required, the consistency of coordinates suddenly becomes important. Even a slight misalignment between the background map and the positions of observation points can appear to field personnel as indicating a completely different location. Especially in jobs that require accountability for locations—such as inspections, patrols, civil engineering, surveying, and infrastructure maintenance—organizing coordinates to match the Geospatial Information Authority of Japan’s base information greatly affects the reliability of heat maps.

The important point here is not to try to accomplish everything using only data from the Geospatial Information Authority of Japan. The most practical approach is to create the heatmap’s "colors" from your company’s or your department’s operational data, and to support the "interpretation" and "spatial alignment" with the Geospatial Information Authority’s open data. If you understand this division of roles, you will be less likely to get confused when choosing data. Decisions about what to use for the background, whether to overlay elevation, whether to check photographs, or whether to use boundaries as auxiliary information will be clarified, making it easier to create a heatmap that fits your purpose.

Basic steps for creating a heat map using the Geospatial Information Authority of Japan's open data

When creating heatmaps using the Geospatial Information Authority of Japan's open data, the rule is to start by clarifying the objective rather than immediately applying colors. The first thing to decide is what you want to visualize. Whether you want to see concentrations of counts, incidence density per unit time, the bias of outliers, or relationships with elevation or slope will completely change the heatmap you should create. If this definition is vague, you may end up with a visually pleasing but unusable figure.

Next, prepare the data that will serve as the basis for the colors. In most cases you need coordinates and values for each point. The points can be in latitude/longitude or in planar coordinates, but it is important to match the format to the Geospatial Information Authority of Japan open data you use as the background. For the values, be explicit about whether they are simple counts, weighted numbers, averages, or cumulative values. If you perform ambiguous aggregations here, you will end up with a heat map that cannot be interpreted later. For example, if the same case has different importance depending on the task, consider weighting rather than a simple count of occurrences.

Then determine the target area. If the target area is too wide, contrasts will be averaged out; if it is too narrow, only local biases will be emphasized. Set the area according to the decision-making unit—such as municipality, construction section, watershed, or patrol area—so the heat map is more directly applicable to practical work. Load the Geospatial Information Authority of Japan’s open data as the background, and adjust the display to a scale appropriate for the target area. At this stage, decide whether it is intended for wide-area use or for on-site verification so that later adjustments remain consistent.

Next, you load the point data and configure the visualization method. Heatmaps often use a representation that spreads influence outward from each point. At this stage, setting the radius of influence is important. If the radius is too large, the whole map becomes blurred; if it is too small, it will look like nothing more than a cluster of points. The appropriate spread varies depending on whether you want to see fine-grained distributions in urban areas or broad regional trends. Here, while viewing the Geospatial Information Authority of Japan’s base map, it is important to choose a spread that does not look unnatural relative to the scale of roads, rivers, facilities, and terrain.

Next, adjust the color gradations and the upper and lower bounds of the shading. A heatmap’s impression can change dramatically with the choice of colors, but in practical work you should prioritize minimizing misinterpretation over flashiness. You need to set it so that differences between areas near the minimum value and areas near the maximum value are distinguishable, while intermediate trends are not obscured. If extreme maximum values occur only in a small area, the whole map may appear washed out. In that case, check the distribution of values and, if necessary, review the aggregation method or the display upper-limit settings. The Geospatial Information Authority of Japan’s open data can help you understand the background, but the color design itself depends on the user’s analytical judgment.

Next, overlay auxiliary data such as elevation and terrain information to confirm the line of reasoning behind your interpretation. Whether the darker-colored areas are aligned along the lower parts of slopes, concentrated in valley channels, continuous along roads, or dispersed across flat areas will change the hypotheses about the causes. Creating a heat map is not the end; the step of examining its relationship with the background is indispensable. If you carry out this step carefully, you move beyond mere visualization to an explainable analysis.

Finally, prepare the output format for on-site verification and stakeholder sharing. The amount of information required varies depending on whether the figure is for a meeting, for taking to the field, or for attaching to a report. By clarifying the scale, legend, target period, aggregation criteria, type of background, and coverage area, the heat map will be less likely to be misunderstood later. One of the values of using the Geospatial Information Authority of Japan's (GSI) open data is that it makes this kind of sharing easier. Visualizing on a base that makes spatial relationships easy for anyone to understand turns the heat map from a diagram only for analysts into one that connects field staff and managers.

Common Data Processing Pitfalls in Practice

A common stumbling block when creating heatmaps is not the step of placing points on a map, but the prior data processing. In particular, in practical work raw data is rarely already in a form suitable for heatmaps, so you need to organize location information, time information, aggregation units, missing values, duplicates, and outliers. If you skimp on this, the visualization may look plausible yet lead to incorrect interpretations.

First, what I want to confirm is the positional accuracy and how well the locations align. If the source data are mixed—address-based records, visually entered points, device location results, coordinates from existing registers, etc.—positional variation can become large. A heat map is a visualization that shows density bias, so even shifts on the order of tens to hundreds of meters (tens to hundreds of ft) can change where the darker and lighter areas appear. Placing the Geospatial Information Authority of Japan’s open data in the background can actually make these shifts stand out more, because the more accurate the background, the more the coarseness of the source data is exposed. Therefore, when mixing data with different levels of positional confidence, you need to handle them with an understanding of the differences in accuracy.

Another thing to watch for is duplicate points. In patrol logs and inquiry histories, multiple records may be left for the same location. If you plot these as-is as points, you'll get a heat map that indicates high frequency, but it becomes difficult to distinguish whether records are simply concentrated at the exact same spot or whether the surrounding area has many problems. In practice, it is important to decide in advance whether to aggregate by location or to treat occurrences as incident frequencies that include time. If you haven't defined what counts as a single incident, the meaning of the heat map becomes ambiguous.

Handling time is also important. A heat map is a figure showing spatial distribution, but if the source data includes time, the setting of the time period alone can greatly change the shading. Phenomena with seasonality, temporary construction impacts, post-disaster concentrations, or increases in inspections during specific periods can be obscured by annual aggregation. Conversely, looking at only a short period can lead to misinterpreting a temporary bias as a persistent trend. In heat maps created using the Geospatial Information Authority of Japan’s open data, differences in period settings are strongly reflected in the results because the background is stable. Always specify the target period, and when making comparisons, align the conditions.

Furthermore, value normalization cannot be overlooked. If you color by raw counts alone, areas with large surface area, high population or many facilities, or areas with high usage frequency will be advantaged. The aggregation method should change depending on whether what you really want to see is absolute counts, density per unit area, or incidence per unit of usage. For example, when turning on-site patrol anomaly reports into a heatmap, it is natural that locations with more patrols will appear to have higher counts. To avoid misreading this as a greater number of problems, processing that takes the denominator into account is necessary. Because heatmaps are such a visually powerful representation, the design of the denominator is extremely important.

Be careful not to overdo interpolation. If you spread smooth colors over a wide area despite having few point data, it can make nonexistent trends appear. Because the Geospatial Information Authority of Japan’s open data has a well-prepared background, smooth color surfaces can make it look as if analysis has been performed, but if the source data are coarse, the representation should be limited to that level of coarseness. Especially when observation points are sparse, the smoothness of a heatmap can exceed the reliability of the analysis. A balance between readability and integrity is necessary.

Finally, the clear labeling of legends and conditions should also be considered part of data processing. A heat map that does not indicate which values are shown in which colors, what the time period is, whether points are weighted, or what background was used carries a risk of being misread the moment it is shared. It may be acceptable if the chart is viewed only by the practitioner, but when it is shared with managers, clients, field staff, or other departments, explaining the conditions is essential. Because heat maps look easy to understand at first glance, their conditions tend to be omitted — and that is where the pitfall lies.

How to Read Heat Maps and When to Use Them

To read a heatmap correctly, it's important first not to equate darker areas with "important places." The color intensity merely indicates that the configured values are relatively or absolutely high. What that high value means depends on the definition of the source data, the aggregation method, the time period covered, and the background conditions. In practice, you should not stop at observing color gradations; you need to interpret their meaning step by step.

The first thing to look at is the pattern of concentration. Whether it is strongly concentrated at a single point, extends in a linear fashion, or spreads out over an area will change the factors you need to consider. If it is concentrated at a point, the influence of a specific facility, an intersection, an observation point, or a hub may be suspected. If the distribution is linear, consider the influence of roads, rivers, slopes, or transmission lines. If it spreads out as an area, land use, broader topographic conditions, or differences in operational range may be underlying factors. Using the Geospatial Information Authority of Japan's open data as a background makes it easier to interpret these patterns.

What you should look at next is the relationship with boundaries. Whether the shading switches at area boundaries or varies along natural topography changes the nature of the cause. If the color changes the moment the management area changes, differences in operations or recording methods may be influencing the result. Conversely, if it changes along valleys, ridges, lowlands, or uplands, the influence of natural conditions is likely stronger. Heat maps show the distribution of a phenomenon, but only when overlaid on background layers does it become easier to form hypotheses about the causes.

One application is setting investigation priorities. Instead of viewing all locations as having the same concentration, narrowing down priority verification areas based on uneven distributions enables limited personnel to carry out on-site checks efficiently. For inspections, patrols, maintenance, complaint responses, and post-disaster checks, heat maps serve as materials to assist initial response decisions.

It is also effective for understanding trends. By comparing heat maps by month, season, or year, you can see changes in distribution. Movements, expansions, contractions, and dispersals that are hard to notice from tables alone can be grasped intuitively on a map. If you use open data from the Geospatial Information Authority of Japan as a background, it becomes easier to consider which elements of topography or land use the changes are related to.

In the practice of civil engineering, surveying, and maintenance, there are also uses for examining the relationship between terrain conditions and on-site work. For example, by viewing on a map clusters of inspection points, sections that are difficult to pass, uneven distributions of repair requests, and locations prone to missed checks, you can often identify opportunities to improve field operations. Heat maps are useful not only as final report maps but also for designing site movement flows and for hypothesis testing during the planning stage.

Furthermore, they are well suited to aligning stakeholders' understanding. There are many situations where displaying a distribution makes comprehension faster than describing in words whether values are high or low. In particular, staff who know the site well find it easier to offer opinions when the distribution is overlaid on a background map, as they can connect it with their experiential knowledge. Heat maps are not meant to draw conclusions on their own; they function as a common language linking field knowledge and analytical knowledge.

Common Pitfalls and Countermeasures When Creating Heat Maps

One common mistake when creating heat maps is that the coordinate systems of the background map and the point data are not aligned. Even if the offset looks small visually, it can lead to critical misunderstandings in the field. Especially in places where positional meaning is important—along roads, near facility boundaries, or along rivers—a misaligned heat map can cause incorrect decisions. As a countermeasure, first check the overlap using reference points or known locations, and visually confirm the alignment between the background and the points.

The second mistake is making the colors too flashy, which makes the mid-range unreadable. If you design to emphasize only the high areas, moderately important areas that are candidates for improvement get buried. In practice, people don't necessarily look only at the maximum values. Rather, it is often the widespread moderate bias that becomes the target for improvement. Color gradations need to be designed so that both extreme values and mid-range trends can be read.

The third issue is presenting a smooth surface despite having little data. When there are few observation points, heatmaps tend to depict a smoother distribution than reality. This can make it appear as if there is continuity that doesn’t actually exist. As a countermeasure, when the number of points is small, keep the heatmap’s range settings conservative and, if necessary, also check the distribution of the points themselves. It is important not to prioritize readability alone.

The fourth issue is the visualization of counts without considering the denominator. Locations with many users, places with frequent patrols, and locations where incidents are more likely to be recorded will naturally show higher counts. If you interpret this directly as indicating a greater number of problems, you will misprioritize countermeasures. As a countermeasure, consider what should be used as the denominator—per unit area, per unit of use, per number of patrols, etc.—and use counts and rates appropriately according to your objectives.

The fifth is treating a heat map as a conclusion. A heat map is a diagram that shows trends, and it does not automatically indicate causes or countermeasures. Simple judgments such as “darker color means danger” or “lighter color means safety” are precarious. It is essential to interpret it together with background topography, usage conditions, recording methods, and time-period conditions. As a countermeasure, position the heat map as a tool for generating hypotheses and use it on the premise of on-site verification and cross-checking with other sources when necessary.

The sixth issue is insufficient explanation of conditions when sharing. Assumptions that seem obvious to the creator are often not conveyed to the recipient. If the target period, the definition of the source data, whether weighting was applied, the meaning of the background, or the color standards are omitted, different stakeholders will develop different interpretations. Because heat maps look easy to understand at first glance, people tend to assume no explanation is necessary, but in reality sharing the assumptions is extremely important. You should understand that completing a figure and turning it into a document that can be used in operations are two different things.

Operational approach for connecting heat maps to on-site verification

What truly adds value in practice is not creating a heat map itself, but being able to decide, based on the heat map you created, what to check on-site. A heat map only becomes a tool for operational improvement when it doesn't end with viewing shades of color at a desk, but connects to deciding which locations to prioritize, in what order to visit them, what to check, and how to feed those findings back into the records.

First, what you should consider is narrowing down the priority locations for inspection. Rather than visiting every dark-colored point, overlay the distribution of intensity with terrain conditions, accessibility, and historical records, and assign priorities for verification. For example, where there is a single, extremely dark spot versus an area where moderate intensity continues widely, the purposes of on-site verification differ: the former focuses on confirming local factors, while the latter centers on examining areal trends. Using the Geospatial Information Authority of Japan’s open data as a background makes it easier to envision how to access the site and the surrounding environment.

Next, the heat map needs to be converted into an on-site verification map. Overview maps used in meetings are often difficult to use in the field. On site, understanding roads, landmarks, changes in terrain, and the work area is important. Therefore, the heat map for broad situational awareness and the detailed map for on-site verification should be treated as having separate roles. Even with the same source data, changing how it is presented depending on the recipient can greatly affect ease of operation.

Furthermore, a mechanism is needed to feed verification results obtained on site back into the next heat map improvements. A heat map is not something you create once and finish; it should increase in accuracy by reflecting what was confirmed in the field. For example, there are things that can only be understood on site, such as recorded positional shifts, duplicate entries at the same point, the effects of terrain, missed patrols, and phenomena that occur only under specific conditions. By returning these to the source data, the next heat map will more closely reflect the actual situation. The idea of iterating between visualization and on-site verification is important.

In civil engineering and surveying work, there are situations where a heat map alone does not provide certainty about a location. Even if the distribution is visible in the background, if it is ambiguous which point on the actual site is being indicated, verification work becomes inefficient. In such cases, methods that link the shading seen on the map to higher-precision position confirmation are effective. In particular, when you want to accurately fix coordinates on site after gaining a broad-area understanding using existing open data, improving the accuracy of location information directly leads to greater work efficiency.

In that respect, operating heat maps using the Geospatial Information Authority of Japan's open data pairs well with the concept of high-precision positioning like LRTK. At a wide-area level, you can grasp trends against the background of open data, and on site you can proceed with checks while confirming positions with an iPhone-mounted GNSS high-precision positioning device, which makes it easier to smoothly connect desktop analysis with field work. Use the heat map to decide "where to look," and let LRTK support "how to secure that point in the field," so that the practical workflow is not interrupted. This combination is especially practical on sites that prioritize control point verification, understanding on-site coordinates, improving the recording accuracy of inspection positions, and streamlining simple surveying.

Summary

What is important in understanding how to create heat maps using the Geospatial Information Authority of Japan's open data is not to treat a heat map as merely an attractive image. The shading of colors changes meaning depending on the definition of the source data, aggregation methods, the target period, coordinate alignment, and background conditions. For that reason, the Geospatial Information Authority of Japan's open data should be used as a foundation for correctly understanding location and terrain, rather than simply as material for applying colors.

To create a heat map that is useful in practice, you need to clarify the objective, organize the data that will determine the colors, consider which publicly available information to overlay as the background, and, after creating it, always connect it to interpretation and on-site verification. By following this process, a heat map transforms from mere visualization into a map resource that supports decision-making.

And if you can design a workflow that uses the Geospatial Information Authority of Japan's open data to grasp broad trends and then secures positions on site with high accuracy for verification and recording, the accuracy and efficiency of operations will improve further. In particular, when you want to verify priority points extracted by a heat map on site, or proceed smoothly with control-point surveying, on-site coordinate checks, or simple surveying, incorporating an iPhone-mounted GNSS high-precision positioning device such as LRTK makes it easier to integrate desk-based analysis and fieldwork. Looking beyond just creating a heat map to actually using it fully in the field is becoming increasingly important in practice.