How to Display Congestion Trends as a Heatmap on GSI Maps

Table of contents

• Concept for visualizing congestion trends on GSI maps

• Organizing the data needed for heatmap display

• Basic procedure for using GSI maps as a basemap

• How to prepare congestion data as point data

• Practical approaches to creating heatmaps

• Why appearance changes with color scales and aggregation units

• Common pitfalls in practice

• Sources and attribution to note for public operation

• Summary

Concept for visualizing congestion trends on GSI maps

Many practitioners who search for “heatmap GSI maps” want to overlay gradations of congestion on a map to intuitively understand where people or vehicles tend to concentrate. Examples include visitation trends at tourist sites, lingering around facilities, crowd-prone routes at event venues, concentration trends around shelters during disasters, and pedestrian flows in urban areas. However, a common initial misunderstanding in practice is thinking that GSI maps will automatically create congestion heatmaps. GSI maps are easy-to-use basemaps, and GSI Maps provides features such as regional mesh display, file import, and external tile import. Therefore, if you organize your congestion data and then overlay it on the map, you can build a visualization environment that is sufficiently useful for practical work.

It is important not to interpret congestion solely as “the number of people present at a given moment.” Congestion trends should encompass not only density at a given time but also time-of-day biases, day-of-week differences, seasonal variations, changes due to events, differences between entrances and exits, and distinctions between lingering and passing-through. Simply coloring areas red is not inherently useful; the accuracy of judgment depends greatly on what you aggregate and at what units you map them. What practitioners really need, beyond whether a place is crowded, is information that leads to operational decisions: where, when, why, how much, and whether crowding persists. Therefore, creating a heatmap should be treated not as cosmetic work but as operational design that includes the design of location information, organization of aggregation criteria, overlaying on the basemap, and adjustment of color representation.

Another advantage of using GSI maps as a basemap is that you can read congestion trends while checking spatial context such as roads, terrain, building layouts, elevation cues, and regional boundaries. Phenomena that are easy to miss in standalone tables—like people lingering before a slope, concentration at bridge entrances, bias toward a station square, or bottlenecks along narrow riverside paths—are easier to understand at a glance on a map. Visualizing congestion trends uses maps not as decoration but as a tool to decipher spatial causes. Whether you adopt this mindset at the outset will greatly affect the practical value of the completed heatmap.

Organizing the data needed for heatmap display

The first thing to organize when displaying congestion trends as a heatmap is which data you will use as evidence of congestion. In practice, candidates include people-flow logs, pass-through counts, entry/exit records, geolocated on-site observations, vehicle traffic counts, facility usage counts, aggregated locations of inquiries or reports, and geolocated visitor survey responses. The crucial point is whether the source data indicating congestion contains both location and counts. If location is vague it cannot be placed on a map, and if there are no counts or weights you cannot determine gradation. In other words, at minimum you need location information showing where something occurred and a numeric value representing the degree of congestion at that location.

A common pitfall is trying to use a list of addresses as-is. GSI Maps can read CSVs that include not only latitude/longitude but also address columns. However, when creating points from addresses they may become representative points and may not indicate the exact real-world location. The required precision varies depending on whether you need building-level resolution, block-level resolution, or down to a corner of a plaza. Small location offsets in congestion analysis can lead to incorrect judgments, so from a practical standpoint it is safer to prepare point data that include latitude and longitude whenever possible. The GSI Maps manual similarly notes that while CSV import can use latitude/longitude or address columns, points created from addresses may be representative points and may not necessarily indicate the precise location of the specified address.

Moreover, a time axis is indispensable for congestion data. Situations at weekday mornings, weekday evenings, weekend middays, during events, or in disasters can vary significantly at the same location. Because heatmaps are visually striking, combining all periods into one image can make the result vaguer than reality. For example, displaying a road crowded only during commute hours and a tourist spot crowded all day on the same map makes it difficult to use for decision-making. In practice, you should decide in advance how to separate by time-of-day, day-of-week, season, and compare normal versus special periods before mapping. The way you separate these depends on whether you want to read general trends, identify peak-time hazards, or review facility layouts.

Additionally, what numeric measure you use for congestion matters. Simple counts may suffice in some cases, while weighting by dwell time may better reflect reality in others. A location with many passersby but no lingering has a different meaning than a location with fewer people who stay longer. Definitions of congestion vary across pedestrian spaces, commercial facilities, tourist spots, shelters, and construction site entrances. Therefore, before creating a heatmap, you must verbalize what will determine color intensity—counts, dwell time, pass-through frequency, number of people lingering, congestion index, etc. If you skip this organization, you may create a map but be unable to explain the results.

Basic procedure for using GSI maps as a basemap

There are two major ways to use GSI maps as a basemap for congestion trends. One is to open GSI Maps and load your own point data or external data directly onto the map. The other is to use GSI tiles provided by the Geospatial Information Authority of Japan as a basemap and overlay your own visualization process. GSI distributes GSI tiles as XYZ-style tiled data, which is a format easy to use in many web mapping systems. This makes them convenient to handle as basemaps.

A simple method is to open GSI Maps, display the target region, choose a pale basemap or aerial photograph basemap as needed, and overlay point data. The GSI Maps manual states that you can read files from the “Drawing/Files” tool and display KML, GeoJSON, GeoTIFF, CSV, and so on. Drag-and-drop display is also supported, making it suitable for prototyping and checks. Loaded data can be edited, so it’s easy to confirm point information and fine-tune display settings.

If you want to operate congestion heatmaps as a business system, it is easier to manage by separating the basemap and the congestion layer. The manual notes that GSI Maps can read external tiles distributed in the same XYZ format as GSI tiles. In other words, if you prepare congestion layers in advance as image tiles or similar, you can overlay them on GSI Maps. This structure is particularly useful for intra-agency sharing and workflows requiring periodic updates, wide-area continuous monitoring, or switching layers by period.

For verification work, regional mesh display is useful. GSI Maps allows you to toggle regional mesh display from the grid display menu, so you can use it not only to show points but also as a reference when considering values aggregated at mesh units. Because displaying individual trajectories can make congestion hard to read, summarizing into a certain range makes it easier to grasp overall trends. Especially in administrative practice and facility management, aggregated results by mesh or parcel are often easier to explain and share than detailed individual records. The fact that GSI Maps has a regional mesh display function is an advantage that aligns well with such aggregation design.

How to prepare congestion data as point data

Before mapping congestion trends, the way you prepare point data is where practical distinctions matter most. If you do this sloppily, no amount of map styling will produce a meaningful heatmap. Basically, organize so that each row corresponds to one point or one observation unit, and include latitude, longitude, timestamp, count or weight, and categories as needed. Categories such as weekday vs. weekend, pedestrian vs. vehicle, entry vs. exit, normal day vs. event day are useful in later steps.

For example, if you want to see congestion trends in a commercial area, simply listing facility names and addresses is insufficient. Whether you want to view by entrance, by intersection in a passageway, or represent an entire plaza with a single representative point changes how you place points. Heatmaps create density from nearby points, so coarse representative points can make dispersed congestion appear concentrated in one place. Conversely, adding excessively many fine-grained points increases noise and obscures overall trends. Therefore, decide the point granularity according to the analysis objective.

Point data always require preprocessing. Typical problems are mixed coordinate systems, blank fields, duplicates, extreme outliers, and inconsistent timestamp formats. Unify the column names for latitude and longitude. GSI Maps’ CSV import recognizes header names such as “緯度”/“経度” or “lat”/“latitude”/“lng”/“lon”/“longitude” and expects decimal notation values. Aligning to this specification reduces rework at the import stage.

When using on-site survey results, watch for observer variation. If standards for judging “crowded” differ among observers, the color intensity will lack consistency. Where possible, convert to quantitative indicators such as number bands, pass-through time, dwell length, or queue length. If quantification is difficult, at minimum standardize the scale—e.g., a five-point rating. Heatmaps are visually powerful, but unclear numerical design behind them undermines their persuasiveness.

Practical approaches to creating heatmaps

In practice, methods for creating heatmaps from congestion data can be organized into three broad approaches. First, overlay points directly and generate density. This suits cases with many people-flow logs or observation points and makes congestion peaks easy to see. Second, aggregate into regional mesh or arbitrary zones and color them. This is suitable for administrative presentations and comparative materials and makes numerical bases easier to present. Third, pre-render the congestion layer as images or external tiles and overlay them on the basemap. This is suitable for broad-area continuous display or high-frequency update scenarios.

Point-based methods are strong for on-site verification but can also mislead viewers. Density representations change substantially depending on radius settings and blur levels, so the same data can create different impressions depending on emphasis. In practice, prioritize settings needed for decision-making rather than those that simply look nice. For example, use a slightly larger radius if you want to see flow bands, or a narrower radius if you want to see localized concentrations of lingering, adjusting according to purpose.

Mesh aggregation is highly effective for explanatory materials. Because GSI Maps can display regional mesh, it is easy to grasp the target area as a grid and use it to consider aggregation units. Especially when showing which districts are dominant, month-to-month changes, or improvements after interventions, mesh color-coding can convey information better than point heat. Think of heatmaps as conveying the feeling of congestion and mesh aggregation as fulfilling accountability; dividing roles this way helps avoid mistakes.

External tile approaches shine in internal portals and maps intended for continuous viewing. GSI Maps can load external tiles by URL, so if you update the congestion layer the basemap remains unchanged and viewers can see new trends. This is particularly effective for weekly or monthly monitoring, maps swapped during event periods, or environments that allow switching among multiple periods. Separating the task of creating maps from the task of operating maps is critically important in practice.

Why appearance changes with color scales and aggregation units

The most common source of misunderstanding with heatmaps is that stronger colors are often perceived as absolutely dangerous, important, or severe. In reality, color intensity is often a relative setting: what range is considered the maximum, where color breaks occur, and how values near zero are treated all change impressions. Therefore, when creating heatmaps of congestion trends, it is not enough for the creator to understand the color criteria; the design must convey those criteria to viewers without causing misinterpretation.

For example, using a single day’s maximum as the baseline can make one outlier appear bright red and obscure differences elsewhere. Conversely, using an all-period average can bury peak dangers. Simply changing the reference population—holidays only, weekdays only, event days only—can alter the color of the same location dramatically. In other words, a heatmap is not a direct photograph of reality but an interpretive map shown through aggregation conditions. Distributing maps without this understanding can lead to incorrect prioritization of countermeasures.

Aggregation units matter similarly. At 10 m (32.8 ft) square units you may see bottlenecks, while at 100 m (328.1 ft) square units you will only see district-wide trends. Fine units are effective where localized lingering is the problem, such as a plaza at a tourist site, while coarse units are easier to read when viewing citywide visitation trends. Choose units—points, meshes, administrative districts, facility units—based on the analysis target. In practice, don’t try to do everything in one map; creating one map for overall trends and separate maps for detailed checks will be more communicative.

The choice of basemap also affects color legibility. Information-rich basemaps help field understanding but can make congestion colors hard to read. Conversely, pale basemaps improve heatmap visibility but may obscure concrete location context. For road congestion, use a basemap where the road network is clear; for facilities, use one where building layouts are easy to grasp; for evacuation guidance, use one where topography and elevation are readable. GSI maps offer a variety of basemap options, so you can switch depending on the type of congestion. Be aware that different GSI tiles have different coverage and zoom levels by type, so confirm the characteristics of the basemap you intend to use in advance.

Common pitfalls in practice

One frequent mistake is a mismatch between source data precision and map precision. If you only have representative points from address data but display as if you have building-entrance resolution, users may overtrust the map. Conversely, if you have high-precision observation data but aggregate it into coarse meshes, you may lose the local detail necessary for countermeasures. Because heatmaps look plausible, you must always be mindful of the alignment between underlying precision and representation.

Another common issue is mixing time series. Mixing tourist season and off-season, normal operating days and event days, or normal and disaster-response periods makes it unclear which scenario the map represents. While a single map may look convenient, effective decision-making requires the courage to separate periods. In fact, displaying multiple maps by period often makes changes and effects of countermeasures clearer.

Privacy and consideration are also important. Treat congestion trends as aggregated data that do not identify individuals; avoid displays so fine-grained that they allow inference of individual actions. Visualization should serve facility operation and regional management, not individual tracking. When making maps public, avoid showing raw points; instead aggregate by mesh or time to produce explainable and considerate representations.

Another often-overlooked issue is skipping on-site verification. Heatmaps are excellent for hypothesis generation but do not confirm causes. Whether a red area truly reflects congestion, an observation bias, or effects of traffic regulation, construction, weather, or route changes often requires field inspection. When using maps in practice, do not stop at mapping; incorporate field checks and link to improvement proposals. Visualizing congestion is not meant to replace field judgment but to enable faster, more accurate field judgments.

Sources and attribution to note for public operation

When using GSI maps as a basemap, do not forget how to handle attribution when publishing or sharing. GSI’s guidance states that when GSI tiles are loaded in real time on a website or application, only attribution is required and no application is necessary. If you edit or process content, in addition to attribution you must indicate that the content has been processed, and you must not present processed information as if it were created by GSI. Congestion heatmaps often overlay user-processed and aggregated information on top of GSI basemaps, so this distinction is critically important in practice.

If you get this wrong, viewers may mistakenly believe that “GSI produced a congestion-assessed map.” In fact, GSI provides basemaps and related data, and the calculation and interpretation of congestion trends are normally the responsibility of the user. Therefore, in your deliverables you must separate the basemap attribution from the creator of the congestion layer and the aggregation conditions. This is important not only legally but also for accountability. Making clear which data were used, which period, which aggregation units, and how coloring was applied is necessary for the heatmap to be trusted as a business document.

Also, adapt your presentation depending on the use case—internal documents, public releases, resident briefings, facility guides. Detailed comparison maps useful for internal review may be inappropriate for public distribution, where coarser aggregation could be more suitable. Overloading the basemap with information can hide the congestion trends that users should actually see. The amount of information needed differs depending on whether the goal is raising awareness, improving flow, or reviewing placement. Practitioners must consider not only mapping techniques but also operational design: at what granularity to show what to whom.

Summary

When considering how to display congestion trends as a heatmap on GSI maps, the most important thing is to organize what congestion means, which spatial and temporal units to aggregate by, and for what purpose you are visualizing—before focusing on the map display itself. GSI maps are very easy to use as basemaps, and GSI Maps offers file import for CSV and GeoJSON, regional mesh display, and external tile import. Therefore, with carefully prepared point data you can use them for everything from quick checks to continuous operation to visualize congestion trends.

However, because heatmaps are visually powerful, they can easily mislead if source data precision, time criteria, color-scale rules, or public explanatory responsibility are insufficient. That is why, beyond relying on the basemap, preparing a workflow that includes confirming point-data precision, validating mesh aggregation, conducting field checks, and clarifying attribution and processing descriptions is the shortest path to deliverables that are useful in practice. Mapping congestion trends is not merely coloring; it is a practical method for spatially interpreting on-site issues.

To truly turn congestion visualizations into operational improvements, do not stop at desk-based visualization: accurately verify on-site where bottlenecks occur and where guidance signs or safety measures are needed. In situations where you want to improve the accuracy of on-site positioning, LRTK that can be attached to an iPhone to perform centimeter-level position confirmation (cm level accuracy (half-inch accuracy)) is effective. Using GSI maps as a basemap to read congestion trends, quickly identifying necessary locations in the field, and advancing decisions on route improvements and safety measures can be applied widely—from tourist sites, event management, and facility management to verifying movement routes around civil engineering sites. When turning map insights into field actions, LRTK is a practical option that supports understanding congestion points and improving the efficiency of on-site inspections.