Six Basic Items to Start Heatmap Analysis with Geospatial Information Authority Data

Practical staff interested in heatmap analysis using Geospatial Information Authority (GSI) data usually do not just want to color a map to make it look good; they mostly want to intuitively grasp terrain and elevation, distribution trends, biases, hazardous spots, and work priorities. In real work, the ability to capture tendencies that are easy to overlook in paper drawings or lists of numbers at a glance by color intensity has great value. Heatmaps are especially effective when you need to understand a wide area quickly or share the situation with stakeholders.

On the other hand, using GSI data does not automatically yield highly accurate analysis results. If it is unclear which data to use, what the colors represent, which coordinate system or elevation datum to use, or how to preprocess, it can rather lead to mistaken judgments. To make heatmap analysis useful in practice, the preliminary design is more important than the drawing itself.

The GSI continuously provides public data usable for analysis, such as numerical elevation models and tiles, and the numerical elevation models in the Fundamental Geospatial Data come in 1 m (3.3 ft), 5 m (16.4 ft), and 10 m (32.8 ft) mesh sizes, while elevation tile specifications are published as development information for GSI Maps. In recent years elevation revisions and updates to coordinate reference systems have also been carried out, so checking update timing and specifications is essential when handling data. GSI online viewing site +3

Table of Contents

• Why heatmap analysis is attracting attention in practice

• Basic 1 Understand the types of GSI data to use

• Basic 2 Decide in advance what the color intensity will represent

• Basic 3 Solidify the flow of data acquisition and preprocessing

• Basic 4 Standardize colorization and interpretation rules

• Basic 5 Have verification procedures to prevent misreading

• Basic 6 Link analysis results to practical decisions and share them

• Conclusion

Why heatmap analysis is attracting attention in practice

The main reason heatmap analysis attracts attention is that it can transform complex numbers and location information into a form that makes on-site decision-making easier. For example, even within the same area, places with large elevation differences, where slope changes abruptly, where density is highly biased, or where outliers are concentrated are hard to intuitively grasp from simple coordinate lists or attribute tables alone. However, when visualized as color intensity and distribution, it becomes easier to see which areas should be prioritized for checking, which ranges are non-uniform, and which zones need additional confirmation.

Many people who search for "heatmap GSI" are looking for map representations usable in practice rather than academic visualization theory. Use cases vary—disaster prevention, facility management, infrastructure inspection, land use review, terrain understanding, preparatory work for site checks—but they share the need to "see a wide area quickly in a form that stakeholders can share as a common understanding." Therefore, when starting heatmap analysis, priority should be given to creating maps that aid judgment rather than merely making pleasing-looking figures.

Another advantage of using GSI data is that, as a public foundation, it is widely used and makes it easier to standardize background conditions. When overlaying proprietary site data or internally managed location information, referring to established terrain and elevation data as the foundation makes explanations among stakeholders easier. Especially when intending to reuse analysis results for meeting materials or on-site explanations, using public map and elevation information as a base facilitates sharing the premises of the explanation.

However, it is important to note that a heatmap is not the fact itself but a representation that makes facts easier to see. The same raw data can give very different impressions depending on color breaks, aggregation ranges, interpolation methods, and display scale. In other words, while heatmaps are convenient, if created incorrectly they can easily produce strong misinterpretations. That is why the first things to establish are the analysis objectives and the rules of representation. Simply applying color without clarifying those points will not produce an analysis usable in practice.

Basic 1 Understand the types of GSI data to use

The first thing to do when starting heatmap analysis is to understand which GSI-provided data should be used. If this is left vague, the acquired data may not fit the purpose and work will need to be redone.

Among GSI data, those most commonly involved in heatmap analysis are numerical elevation models, GSI tiles, background maps, and aerial photograph data. In particular, when dealing with elevation and terrain trends, the numerical elevation models in the Fundamental Geospatial Data are central. The numerical elevation models are mesh elevation data available in 1 m (3.3 ft), 5 m (16.4 ft), and 10 m (32.8 ft) sizes, and the suitability depends on whether detailed terrain representation or broad trend understanding is the goal. GSI online viewing site

It is important to note that higher resolution is not always the correct choice. Fine data such as 1 m (3.3 ft) mesh makes it easier to grasp local undulations but increases data volume and preprocessing/display load. Conversely, coarser meshes are suitable for grasping broad trends but tend to absorb small terrain changes. In short, you should choose according to the decision-making unit in the field: whether you want to see fine irregularities or area-level elevation tendencies will change the appropriate granularity.

Also, elevation tiles accessible through GSI Maps are very convenient as an entry point for map display and analysis. According to the elevation tile specifications, they are provided in PNG format as 24-bit color, 256 pixels square, and a method to calculate elevation from RGB values is published. This means tiles can be used not only for display maps but also as material for analyses that handle elevation values.

Furthermore, attention to data update timing is necessary. GSI has been progressively updating elevation tiles and numerical elevation models in line with elevation result revisions, and recent notices include updates to post-revision values and changes related to the coordinate reference system. When overlaying past internal materials or existing data, comparing under outdated premises may lead to unnoticed discrepancies affecting judgment.

In practice, the correct order is first to clearly articulate "what you want to see" in words, and then consider "which GSI data can most easily be used for that purpose." Using only background maps when you want elevation differences, or touching only elevation data when you want distribution trends, will not meet the goal. The first step of heatmap analysis is improving the accuracy of material selection before coloring.

Basic 2 Decide in advance what the color intensity will represent

A common failure in heatmap analysis is applying colors to a map without clear purpose. Color is just a means to show differences in numbers or states, and if what it represents is not decided in advance, the map will not be meaningful.

For example, whether you want to represent elevation itself, elevation difference, slope steepness, distance from a specific point, or ease of access will produce completely different heatmaps. Even with the same elevation data, the meaning changes depending on whether the target is "absolute height" or "difference from surroundings." If you make a map without distinguishing whether you want to know high places or rapidly changing places, it may look plausible but be useless in practice.

The first thing practitioners should decide is the analysis unit: point, line, or area. Or whether to aggregate by a fixed mesh or average by existing zones. If this unit is not determined, the heatmap granularity will vary. Too fine and noise stands out; too coarse and differences disappear. Particularly when combining public foundational data like GSI data with proprietary or site-specific data, aligning aggregation units is indispensable.

Next decide the meaning of the values. Does a darker color mean higher, more dangerous, more biased, or higher priority for confirmation? If this convention is ambiguous, interpretations will diverge among stakeholders. Although heatmaps appear intuitive, the design of the legend and evaluation axis is extremely important. Because many people judge by color impression alone, creators must define "what this color represents" in advance.

Also, for operational use, avoid packing too many analytic goals into one figure. Trying to display elevation, slope, density, hazard level, and priority all in one heatmap makes it harder to read. It is better to give one meaning per figure first and, if necessary, place other figures side by side for comparison; this reduces practical misunderstandings.

Many search users assume "you can analyze immediately if you use GSI data," but in reality, once you can verbalize "what you want to visualize," half the analysis is already done. Heatmap work is not merely drawing; it is also organizing decision axes. Not skipping this step is the most certain way to avoid rework later.

Basic 3 Solidify the flow of data acquisition and preprocessing

To perform heatmap analysis consistently, it is important to standardize the approach from data acquisition to preprocessing each time. The quality of preprocessing, more than the drawing itself, determines the reliability of the results.

When downloading and using GSI Fundamental Geospatial Data, it is currently indicated that user registration and login are required on the download page. Such operational conditions directly affect business workflows, so determine who will obtain the data, at what unit it will be stored, and who will replace it upon updates.

In preprocessing after acquisition, the first step is clipping the target area. Processing an excessively wide area increases the load, while clipping too narrowly prevents comparison with surroundings. In practice, it is easier to judge if you include a margin to observe surrounding influences in addition to the analysis target area. Especially for slopes and valley terrain where surrounding topography has a large influence, clipping only the target area can lead to misreading trends.

Next is handling missing and anomalous values. Elevation tile specifications publish how to treat invalid values, and if such values are processed as normal elevation they will appear as unnatural extremes in a heatmap. Deciding in advance how to exclude invalid values, outliers, and areas outside aggregation targets will greatly change the map’s reliability before analysis.

Furthermore, alignment of coordinates and reference surfaces is indispensable in preprocessing. Recently GSI-provided data have notifications about elevation revisions and the change to JGD2024, and when combining data from different times or existing drawings, you must always confirm whether they are being compared on the same basis. Overlooking this may cause color differences on the heatmap to stem from datum differences rather than terrain changes.

What practitioners should focus on in preprocessing is preparing data to be comparable, not making it look clean. Only when range, granularity, missing-value handling, reference, and update timing are aligned does a heatmap have meaning as an analysis document. Conversely, no matter how polished a map looks, if these are not aligned it is a weak decision-making document.

Also, once preprocessing is proceduralized it gains reusability. Instead of starting from zero each time, standardizing the sequence "acquisition," "clipping," "consistency check," "missing-value check," "aggregation," "display" helps maintain quality even if the person in charge changes. If you want to operate heatmap analysis continuously, shift from the mindset of creating a single figure to embedding it in a reproducible business flow.

Basic 4 Standardize colorization and interpretation rules

Color is the most eye-catching element in a heatmap. Therefore, color design determines how analysis results are conveyed. In practice, this is where decisions tend to be most intuitive and later produce misunderstandings.

First, make the relationship between color intensity and values monotonic. Will higher values be darker, will dangerous conditions trend toward warm colors, or will larger deviations from the mean appear stronger? If this relationship swaps midway, users will misread the map immediately. Because practical heatmaps are often judged at a glance, avoid color designs that contradict intuition.

Next, the way the legend is segmented is important. Mechanically dividing values evenly from minimum to maximum may not reveal differences meaningful in practice. For instance, whether you need to know "locations that exceed a threshold" or "relatively high places within the whole" affects how you partition the scale. Decide the color steps according to the analysis purpose. Prioritize ease of judgment over visual smoothness.

Also carefully consider whether to use the same color scheme for wide-area and detailed maps. Differences that appear small at a large scale can look strong when zoomed in locally. Conversely, applying thresholds intended for detailed maps to wide-area maps can result in nearly uniform colors. Therefore, adjust the legend according to map purpose and scale, and clearly state which rules were used for coloring on each map.

In colorization, emphasize comparability over aesthetics. When comparing month-to-month, zone-to-zone, or year-to-year, it is often better to unify legends for maps created under the same conditions. If the color baseline changes each time, you cannot tell whether differences are real or just changes in display rules. If heatmaps are used for regular reporting, design them to preserve comparison axes.

Moreover, how you overlay the background map greatly affects readability. A strong background can drown out color differences; conversely, stripping the background can make spatial relationships hard to grasp. Since heatmaps are color overlays on maps, adjust the amount of background information (terrain, facility layout, roads, waterways, etc.) according to what you want readers to see. The optimal overlay depends on whether the analysis purpose is "visualizing trends" or "explaining locations."

Ultimately, colorization is not decoration but translation of judgment criteria. Because it is the task of translating numbers into a form readable on site, do not rely on creators’ intuition alone. If you want to institutionalize heatmaps within a department, invest effort in sharing the color rules rather than the colors themselves.

Basic 5 Have verification procedures to prevent misreading

While heatmaps are easy to understand, their apparent clarity can lead to misreading. Therefore, post-drawing verification is indispensable. A heatmap distributed without verification will accelerate wrong decisions rather than speed up correct ones.

First, cross-check with the source data. Areas where colors change abruptly, spots that unnaturally differ from surroundings, or places where there is a sudden discontinuity near boundaries should always have their original values verified. If you judge without determining whether such features are actual terrain changes, effects of missing-value processing, differences in aggregation units, or datum discrepancies, you will be swayed by the heatmap’s impression.

Next, check how the appearance changes with scale. Biases that are clear at one scale may disappear when zoomed in, and vice versa. Heatmaps are both analysis results and display results. Observing how impressions change with display resolution and map scale reduces misunderstandings in meeting materials and reports.

Also, having at least one comparison target is effective. For example, the same area at a different time, a separate area under the same conditions, or the same data aggregated by a different unit—

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