Population projections, hazards, urban planning and land use on one time-series screen. No cross-department data hunts or GIS training — ask the AI and assemble the evidence for location-optimisation plans, disaster plans and council briefings.
Sound familiar?
Examples of problems local governments and research institutes may face.
Planning’s GIS, disaster’s hazard maps, our own population stats — just overlaying them is a project.
With MAPRISE
Zoning, location-optimisation zones, hazards, population, transport and facilities shown together — cross-department analysis in one browser tab.
Just ask the AI
“Where do population decline and flood risk overlap?”
Cross-department data
Weeks of coordinationOne browser screen
We spend more time downloading and cleaning data than analysing it.
With MAPRISE
Census, population projections and national land data, layered with sources attached — no more downloading and processing in a desktop GIS.
Just ask the AI
“Rank the districts by population growth and decline”
Prep before analysis
Data collected and merged by handCompared on the map right away
I want to show residents, simply, what this district looks like in 20 years.
With MAPRISE
Compare aerial photos, DIDs, land use and population projections over time, and use zone × population analysis to prioritise measures.
Just ask the AI
“What happens to population in the residential guidance zone by 2050?”
Time span
1960–2070
I need data on that district before tomorrow’s council session.
With MAPRISE
Confirm the district’s data through the AI, then export a PDF with maps, charts and tables — sources added automatically.
Just ask the AI
“Give me three key points on this district for the council”
Briefing
Sourced PDF
* Comparisons illustrate how the workflow changes; they are not measurements. Actual results vary by environment and usage.
Before and after
Places to check
Downloaded one by one, merged in Excel/GIS
Regulations
Hazards
Prices & population
From data check to briefing
* Comparisons illustrate how the workflow changes; they are not measurements. Actual results vary by environment and usage.
Nothing to learn
No menus or jargon to memorise. Ask in your own words and the AI looks up the data on the map and answers — qualitative questions like "what kind of area is this?" and quantitative ones like "by what percentage did it change?" alike.
No training needed
Enter an address and say what you want to know. No manuals or training sessions.
Ask how to use it, too
Ask "how do I show flood depth on the map?" and the AI walks you through it.
Qualitative and quantitative
Get both the narrative ("what is this area like?") and the numbers (percent, people, scores), with charts.
A conversation with MAPRISE AI
Where do population decline and disaster risk overlap?
In the south of the city, fast-declining mesh cells overlap the flood-inundation area — a candidate for reviewing the residential guidance zone.
What is this area’s population in 2050?
Calculated from population projections.
Summarise three points for a council briefing
① The centre holds its population ② Suburban decline accelerates ③ Residents in flood zones need measures.
How many people live in high flood-risk areas?
Residents tallied by expected flood depth.
* The conversation illustrates the shape of the answers; figures are examples unless quoted from a real screen. AI answers are for reference only — please confirm important decisions with a specialist.
By the numbers
Share of municipalities that answer their GIS usage is "insufficient" — substantial room for inter-departmental integration.
Source: MLIT, "GIS Action-Plan Follow-up Survey" (estimate)
Year by which Kyushu's population is projected to fall to ~75% of today — a required evidence point for Location Normalization Plans.
Source: Vulcanz LLC original calculation based on MLIT "Future Population Projections (National Land Numerical Information)" R6 edition
Nationwide coverage of MAPRISE's Location Optimization Plan areas (residential- and urban-function inducement districts overlay simultaneously). Tottori and Okinawa are excluded — source data not yet published.
Source: MLIT National Land Numerical Information (A55)
Start year of MAPRISE's DID (densely-inhabited district) timeline. 12 snapshots compare urban growth and decline through history.
Source: National Land Numerical Information A16 (MLIT)
| Scenario | Before | After (MAPRISE) |
|---|---|---|
| Cross-department analysis | Each section's system referenced separately; manual Excel integration | Urban planning + disaster + population + transit shown together on one screen |
| Location Normalization Plan drafting | Outsource the investigation to specialist consultants | Urbanisation-guidance districts + future-population projection overlaid; evidence brought in-house |
| Disaster-plan revision | Reference each municipality's hazard map separately | Nationwide hazard + shelter + population distribution unified |
| Council / public-meeting materials | Static PDFs, paper-based explanations | Time-slider that animates "how the area changes over time" |
| Academic / research data gathering | Download census and National Land Numerical Information separately | One-click sourcing of cited datasets — citation metadata included |
The Before / After above illustrates how the workflow can change with MAPRISE; it is not a measured result. Actual results vary by environment and usage.
| Setting | What MAPRISE does |
|---|---|
| Annual Location Normalization Plan review | Urbanisation-guidance + future-population time series → revision-direction evidence |
| Regional disaster-plan update | Hazard history + shelters + at-risk resident districts unified |
| Urban planning council prep | Zoning proposal + nearby land prices + population dynamics into a single PDF |
| Academic research (urban shrinkage / DID) | 1960–2015 DID transitions as time series, citation metadata included |
| Council Q&A response | Ask the AI for area-specific data — support for preparing answers to council questions |
Q. Does it integrate with our open-data portal?
A. Automatic integration (API) with municipal open-data portals is not available today (planned for the Enterprise plan; timing TBD). Map data can be exported as CSV (Starter and above; formats other than CSV, such as GeoJSON, are not supported). Bringing in municipal-specific datasets (urban-planning GIS, lot-number maps, etc.) is planned for case-by-case handling under the Enterprise plan (coming soon, timing TBD).
Q. What citation format is used for academic papers / official reports?
A. Each data layer carries source attribution (MLIT, METI, etc.) suitable for citation. Specific citation formatting requirements can be discussed with support.
Q. Can it be used from a closed government network (LGWAN)?
A. We're cloud (SaaS) — a general internet connection is required. LGWAN-ASP arrangements are handled under the Enterprise plan.
Q. Access control / audit logs?
A. SSO/SAML, IP restrictions, and access audit logs are planned for the Enterprise plan.
Q. Can we run a PoC / pilot first?
A. Yes — 3–6 month pilot arrangements are available for government and research institutions. Reach out to discuss.
For government and research workloads we recommend the Enterprise plan.
Note: Enterprise is coming soon (timing TBD). Please contact us to discuss your requirements.
Use case: for location-optimization plans and wide-area administration, grasp the big picture with a population-change ranking of 92 Kyushu cities, then prioritize policy with compact-city analysis crossing optimization zones and demographics.
For academic and government institutions
Our dedicated Enterprise plan for municipalities and research institutions is coming soon (timing TBD). Contact us for onboarding consultations and demos.
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