Sample report: fictional company, real KASPR data

Internet reliability report: Veszprém, Hungary

Based on July 2026 data

Prepared for: Northwind Logistics (fictional example)
Region: Veszprém, Hungary
Data vintageData vintageThe date the data was cut: every figure on the page is from this vintage. Windows such as the past 12 months or the past 90 days end at the vintage, not today.More in the documentation →: 16 Jul 2026
Report date: 22 Oct 2026

All figures in this report describe areas, not individual locations: each location is placed in its district, or in its region or country where KASPR has no districts, and shares that area’s record. The exact address or coordinates of a location do not change its figures.

Summary

3 of your 5 districts had a disruption lasting a week or more in the past 12 months.
Each of your locations is in a district of its own.
Balatonfüred and Sümeg were disrupted at the same time on 1 occasion in the past year.
Balatonalmádi carries the most exposure: 1 locations × 17 disruption days in the past 12 months.

Locations: 5
Districts: 5
Disruptions, past 12 months: 4
Disruption days, past 12 months: 32
Longest disruption, past 12 months: 10 days

Source: KASPR Datahaus, data vintage 2026-07-16

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Where your locations are

Your 5 locations fall in 5 districts.

Districts shaded by grade, locations sized by locations

Grade, A best to E poorestABCDE

Locations are drawn where they were matched to a district; the map shows districts, not buildings.

Source: KASPR Datahaus, data vintage 2026-07-16

Location	GradeGrade A to EThe grade ranks the district by the KASPR Connectivity Score, a 0–100 blend of speed, stability, evening slowdown, reliability and trend over the past 90 days. A is the top fifth of the scored districts in the same country, E the bottom fifth. A district without a score of its own takes its region’s and is marked as estimated.More in the documentation →	Typical latencyTypical latencyThe mean round-trip time, in milliseconds, of KASPR’s probes from its measurement servers to the responsive internet addresses in the district, in a typical week at this data vintage. It is not the delay a user sees to a given website or app, and it is not download speed. It includes the distance to KASPR’s servers, so compare districts within a country rather than across continents.More in the documentation →	Typical stabilityStabilityHow much round-trip times vary across the district’s responding addresses within an hour, in milliseconds, at the district’s typical-week level. Lower is steadier.More in the documentation →	Worst hours (local)Worst hoursThe three hours of the week with the highest modelled variability, in the district’s local time. KASPR models one weekly pattern per country and scales it for each district, so districts in one country share their worst hours and differ in how rough those hours get.More in the documentation →	Disruptions, 12 monthsDisruptions, past 12 monthsA disruption is a fall in the number of responding addresses that KASPR’s anomaly detection classes as a collapse, an outage or a sustained drop. The record is daily, so disruptions are counted in whole days, over the 12 months to the data vintage.More in the documentation →
Depot, Ajka	C	220 ms	95 ms	Wed 15:00, Fri 15:00, Tue 09:00	none
Home agents, Balatonalmádi	C	215 ms	93 ms	Wed 15:00, Fri 15:00, Tue 09:00	2 (17 d, longest 10 d)
Home agents, Balatonfüred	D	321 ms	152 ms	Wed 15:00, Fri 15:00, Tue 09:00	1 (7 d, longest 7 d)
Depot, Pápa	C	207 ms	76 ms	Wed 15:00, Fri 15:00, Tue 09:00	none
Home agents, Sümeg	D	208 ms	73 ms	Wed 15:00, Fri 15:00, Tue 09:00	1 (8 d, longest 8 d)

Source: KASPR Datahaus, data vintage 2026-07-16

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How quality has changed over the past yearTypical latencyThe mean round-trip time, in milliseconds, of KASPR’s probes from its measurement servers to the responsive internet addresses in the district, in a typical week at this data vintage. It is not the delay a user sees to a given website or app, and it is not download speed. It includes the distance to KASPR’s servers, so compare districts within a country rather than across continents.More in the documentation →

Monthly stability and latency, July 2025–July 2026.

The available record ends on 16 Jul 2026. July 2026 is partial; changes compare the first and last complete months within this window.

Stability by month (ms)

AjkaC

−21% over July 2025 – June 2026

BalatonalmádiC

−26% over July 2025 – June 2026

BalatonfüredD

−64% over July 2025 – June 2026

PápaC

−7% over July 2025 – June 2026

SümegD

−25% over July 2025 – June 2026

median of your districts in this region Ticks on the time axis mark the start of each disruption. Lower is better.

Source: KASPR Datahaus, data vintage 2026-07-16

Latency by month (ms)

AjkaC

−7% over July 2025 – June 2026

BalatonalmádiC

−9% over July 2025 – June 2026

BalatonfüredD

−56% over July 2025 – June 2026

PápaC

0% over July 2025 – June 2026

SümegD

−5% over July 2025 – June 2026

median of your districts in this region Ticks on the time axis mark the start of each disruption. Lower is better.

Source: KASPR Datahaus, data vintage 2026-07-16

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Hourly stability and latencyStabilityHow much round-trip times vary across the district’s responding addresses within an hour, in milliseconds, at the district’s typical-week level. Lower is steadier.More in the documentation →

Hourly stability and latency by local weekday and hour, from the July 2026 dashboard release.

The same typical-week estimates shown in Pulse, in local time, summarising historical patterns at the July 2026 release. Monthly charts cover July 2025–July 2026. Districts without their own estimates use regional or country profiles.

Typical day, local time

Latency (ms)

Stability (ms)

Disruption history

Your districts recorded 4 disruptions in the past year across 3 districts; the longest lasted 10 days, in Balatonalmádi.

Disruptions in the past year, shaded by depth

Fall in responding addresses (%)Disruption depthHow far the number of responding addresses fell during a disruption, as a percentage of the district’s usual level. A deeper fall means more of the district’s measured addresses stopped answering.More in the documentation →0 – 100

Source: KASPR Datahaus, data vintage 2026-07-16

Length of each disruption

The record is daily, so a disruption is counted in whole days from first to last affected day.

Source: KASPR Datahaus, data vintage 2026-07-16

All 4 disruptions as a table

District	Start	End	Days	Depth
Balatonalmádi	10 Aug 2025	16 Aug 2025	7	-25%
Balatonalmádi	25 Aug 2025	3 Sep 2025	10	-15%
Sümeg	9 Jul 2026	16 Jul 2026	8	-35%
Balatonfüred	10 Jul 2026	16 Jul 2026	7	-16%

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Which locations go down togetherCo-disruptionTwo of your districts count as disrupted together when their disruptions overlap by at least one day. The report counts these occasions in the past year. A backup location in a district that fails at the same time is not a backup.More in the documentation →

Balatonfüred and Sümeg have been disrupted at the same time most often: 1 occasion in the past year.

A backup location in a district that fails at the same time is not a backup.

Occasions disrupted at the same time, in the past year

Two districts count as disrupted together when their disruptions share at least one day.

Source: KASPR Datahaus, data vintage 2026-07-16

Pairs disrupted together on two or more occasions

No pair has been disrupted together on two or more occasions.

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Where your exposure isExposureEach district’s weight at your locations, multiplied by its disruption days in the past 12 months. The weight is the number of your locations in the district. It ranks where disruption touched most of your locations.More in the documentation →

Balatonalmádi carries 53% of your exposure in the past 12 months.

Each district’s locations at your locations, multiplied by its disruption days in the past 12 months.

Exposure: locations × disruption days, past 12 months

Source: KASPR Datahaus, data vintage 2026-07-16

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Method and limitsArea conditions, not a buildingEvery figure describes the internet in the district, measured from outside the networks on fixed-line infrastructure. It is not a test of any building’s own connection: a location can be better or worse served than its district.More in the documentation →

These figures describe the internet in each district, measured from outside the networks.

KASPR measures fixed-line internet infrastructure from outside the networks, every hour in the past year, and summarises it by district.
Every figure is an area aggregate. A location’s address or coordinates are used only to place it in its district, or in its region or country where KASPR has no districts; the figures are that area’s, and a location can be better or worse served than its area.
Latency is the typical round-trip time. Stability is how much that time varies from hour to hour; lower is better for both.
A disruption is a measured fall in the number of responding addresses that lasts at least a day. The record shows when, where and how deep each fall was.
Disruption figures are whole days, because the record is daily.
Grades A to E rank districts by the KASPR Connectivity Score within their country, A being the country’s top fifth; a country with too few scored districts is ranked against the world.
Districts without enough data of their own are estimated from the surrounding region and say so next to the grade.
Hourly charts use Pulse’s existing typical-week profiles from the July 2026 release. Monthly history covers July 2025–July 2026.

Data vintage 16 Jul 2026. District boundaries: kdh_adm2_fixed-2026-08-13, shown for display only.

Based on July 2026 data. Latest observation: 16 Jul 2026. This sample is not refreshed monthly.

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Next step

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Internet reliability report: Veszprém, Hungary

Summary

Where your locations are

Hourly stability and latencyiStabilityHow much round-trip times vary across the district’s responding addresses within an hour, in milliseconds, at the district’s typical-week level. Lower is steadier.More in the documentation →

Disruption history

Pairs disrupted together on two or more occasions

Where your exposure isiExposureEach district’s weight at your locations, multiplied by its disruption days in the past 12 months. The weight is the number of your locations in the district. It ranks where disruption touched most of your locations.More in the documentation →

Next step

Hourly stability and latencyStabilityHow much round-trip times vary across the district’s responding addresses within an hour, in milliseconds, at the district’s typical-week level. Lower is steadier.More in the documentation →

Where your exposure isExposureEach district’s weight at your locations, multiplied by its disruption days in the past 12 months. The weight is the number of your locations in the district. It ranks where disruption touched most of your locations.More in the documentation →