library(gePoints)
library(readr)
library(dplyr)
library(gt)
zundert <- read_csv(
"text, lat, lon
Achtmaal, 51.45250, 4.58194
Rijsbergen, 51.51694, 4.69611
Wernhout, 51.45583, 4.64250
De Berk, 51.57667, 5.77333
De Lent, 52.44361, 5.74778
Helpt Elkander, 52.00927, 5.93669
't Kapelleke, 51.70250, 5.90725
Klein Zundert, 51.47480, 4.64310
Klein-Zundertse Heikant, 51.45900, 4.64570
Laarheide, 51.45550, 4.70700
Laer-Akkermolen, 51.49500, 4.75150
Markt, 51.45660, 4.64110
Molenstraat, 51.47230, 4.64870
Poteind, 51.48490, 4.61240
Raamberg, 51.44410, 4.65890
Stuivezand, 51.43420, 4.60730
't Stuk, 51.54210, 5.81640
Schijf, 51.49140, 4.54020
Tiggelaar, 51.47230, 4.70270
Veldstraat, 51.47040, 4.61150"
)
gt(zundert) |>
tab_caption(caption = "Hamlets of Zundert") |>
tab_source_note(source_note = "Source: Compiled from municipal records")8 Example: Cultural Geography — Zundert
Vincent van Gogh was born in 1853 in Zundert, a municipality in the province of North Brabant in the southern Netherlands. The landscape around Zundert — flat, agricultural, crossed by roads connecting small hamlets — shaped his early visual vocabulary. The fields, heath, and village life of this area appear throughout his later work.
But Zundert is not only Van Gogh country. Every first Sunday in September, twenty hamlets build enormous floats covered entirely in dahlias and parade them through the town center. The Corso Zundert, now in its eighth decade, is the world’s largest flower parade. Each hamlet, some with only a few hundred residents, spends months designing a float in a communal shed, then covers it with hundreds of thousands of individual dahlia blooms in a final intense burst of work. The result is monumental sculpture in flowers, judged by a panel, fiercely contested, and witnessed by tens of thousands of spectators.
8.1 Geographic intelligence as travel methodology
This example began not as a technical exercise but as trip preparation. After attending the 2017 Corso and being astonished by the scale and artistry of the floats, we wanted to return, not as spectators, but as informed visitors who could engage with the people who build them. The question was practical: where are these hamlets, and how are they distributed across the landscape?
A table of hamlet names tells you almost nothing. The same names plotted on Google Earth, with satellite imagery at field-scale resolution, tell you about distances, road connections, and the physical relationship between the hamlets and the town center. That spatial knowledge becomes a form of intelligence. It lets you plan which construction sheds to visit, estimate travel times between them, and understand the geographic logic of the event: why certain hamlets cluster, why others are isolated, and how the parade route connects them all to the Markt.
We built this map before our second visit in 2025. Armed with it, we went to several hamlets in the days before the parade and visited the assembly areas where volunteers were “tikking” (inserting individual dahlia stems into the float structures). The map was not just wayfinding. It was a credential. When you arrive at a construction shed and can name the neighboring hamlets, ask about the route from their shed to the Markt, and show that you understand the spatial fabric of their community, people open up. We saw construction techniques, design sketches, and behind-the-scenes logistics that casual visitors never encounter.
The principle generalizes. Building a geographic framework before you arrive at any destination, whether a flower parade in North Brabant, a research station network, or an unfamiliar field site, sharpens your questions, reveals structure that would otherwise be invisible, and increases the probability of meaningful interaction with local people. The map is not the end product. It is the preparation that makes everything else possible.
This example maps 21 hamlets in and around Zundert, demonstrating gePoints in a cultural geography context where the point data tells a story about settlement patterns, landscape, and the spatial infrastructure behind a remarkable community event.
8.2 The data
The coordinates cluster tightly — most hamlets fall within a rectangle roughly 15 km by 15 km. This density makes the Google Earth overlay particularly effective, because at the zoom level needed to see all the points, the satellite imagery resolves individual fields and roads.
8.3 Basic KML
create_kml(zundert, "zundert_basic.kml")Twenty-one red pushpins on the Dutch countryside. Even this default view is interesting — the hamlets are not uniformly distributed. Some cluster along roads; others sit in relative isolation.
8.4 Styled version
Use paddle markers with a muted color to complement the landscape rather than dominate it:
zundert_styled <- zundert |>
mutate(
color = "blue",
symbol = "paddle",
symbol_scale = 1.0,
text_color = "white",
text_scale = 0.9,
comment = paste0("Hamlet: ", text,
"\nLat: ", round(lat, 4),
"\nLon: ", round(lon, 4))
)
create_kml(zundert_styled, "zundert_styled.kml")
preview_map(zundert_styled)8.5 What the map reveals
Zoom to the level where all 21 markers are visible and the settlement pattern is clear. The hamlets form a loose network connected by roads that follow field boundaries. Most sit at intersections or along waterways. The landscape between them is a patchwork of agricultural plots — the same fields Van Gogh painted.
Markt marks the center of Zundert proper. Klein Zundert sits just to the north. The hamlets with directional or descriptive names — Stuivezand (drift sand), Laarheide (heath), Raamberg — encode landscape features that are sometimes still visible in the satellite imagery.
For the Corso, the spatial pattern carries operational meaning. Each hamlet maintains its own construction shed, typically at the edge of the settlement. The floats must travel from these sheds to the parade staging area and then through the town center. Hamlets closer to Markt have shorter logistics; those on the periphery face longer transport of fragile, flower-covered structures over rural roads. The map makes this logistical geography legible at a glance.
This is not a scientific dataset in the usual sense, but it demonstrates an important use case for gePoints: mapping named locations from a cultural or historical source onto real terrain. The technique works equally well for archaeological sites, historical landmarks, literary geographies, or any collection of named places with coordinates. And as a tool for travel preparation, it transforms a visitor from a spectator into a participant — someone who arrives with spatial knowledge that opens doors.