7 cartographic explorations · IBGE, CNEFE, Federal Revenue

ミxploring New Cartographic Technologies

Today, through a screen, an individual can interrelate, interact, represent and navigate among millions of data points. Technological advances have drastically expanded our capacity to map territories, networks and flows.

01 · Rio de Janeiro: every dot is a company

It looks like a nighttime aerial photo — but every dot of light is a business establishment registered with Brazil's Federal Revenue. Plotting the location of every active establishment in the National Registry of Legal Entities (CNPJ), the urban fabric draws itself: avenues, commercial centers and peripheries emerge without any base map.

Not an aerial photo: it is the Federal Revenue registry projected onto the territory.

Dark map of the state of Rio de Janeiro formed by millions of luminous dots, each representing an active company registration — Every illuminated dot is a legal-entity establishment. Source: CNPJ registry, Federal Revenue. Click to open the interactive version.

Close-up of the Rio de Janeiro company visualization, with the urban footprint drawn only by establishment dots — Zooming in, the urban footprint appears drawn by the establishments alone — no satellite imagery is used.

Explore the interactive version at ミ.xyz/dataviz/rio/ibge.

02 · Every church in the country

The CNPJ registry records the legal nature of each establishment, which makes it possible to isolate religious organizations and plot them onto the territory. The result is a portrait of the country's religious infrastructure — and of the asymmetries that appear when we compare it with schools, health units and labor unions.

The national map

Map of Brazil formed by dots marking every religious establishment in the country, with high density along the coast and in the Northeast — Every dot is an active religious establishment. Source: CNPJ registry, Federal Revenue.

3D map of the state of Rio de Janeiro with vertical spikes marking the concentration of religious establishments — In three dimensions: the concentration of religious establishments in the state of Rio de Janeiro.

Heat map of the São Gonçalo region (RJ), with purple and yellow dots marking the density of religious establishments — Density of religious establishments in the São Gonçalo region (RJ).

The religious profile of each municipality

Crossing the IBGE Census with municipal locations, the country can be colored by the declared religious profile of its population: the more yellow, the more Catholic the municipality; purple tends to indicate Evangelical predominance.

Map of Brazil in kepler.gl with municipalities colored from yellow (Catholic predominance) to purple (Evangelical predominance) — Declared religious profile by municipality. Source: IBGE Census; visualization in kepler.gl + DuckDB.

More churches than health units

Stacked bar chart by state comparing the share of education, health and religious establishments in the 2022 Census — Share of education, health and religious establishments in each state. Source: IBGE, 2022 Census.

22 of Brazil's 27 states have twice as many religious establishments as health units.

Religious entities overtake labor unions

Line chart of the cumulative number of establishments by legal nature: religious organizations overtake labor unions around the year 2000 and surge after 2010 — Cumulative establishments by legal nature: religious organizations overtake labor unions around the year 2000. Source: CNPJ registry, Federal Revenue.

And the schools?

Visualization of the distribution of school types in Rio de Janeiro, from primary to secondary education — Distribution of school types in Rio de Janeiro (primary, secondary…). Source: IBGE / CNEFE.

03 · Nova Friburgo in three dimensions

The National Registry of Addresses for Statistical Purposes (CNEFE) records every address visited by IBGE. Filtering down to the municipality of Nova Friburgo (RJ), the city can be reconstructed house by house — and each residence extruded by its number of residents.

Residences with height relative to the number of residents

3D visualization of Nova Friburgo where each residence is a column with height proportional to the number of residents — Each column is a residence; its height, the number of residents. Source: IBGE / CNEFE. Click to open the interactive version.

Explore the interactive version at ミ.xyz/dataviz/friba/pop_3d.

Every residence in the municipality

Elongated map of the municipality of Nova Friburgo with all residential addresses plotted as dots — All residential addresses in Nova Friburgo. Source: IBGE / CNEFE.

Where the city concentrates

Dark 3D map of Rio de Janeiro's mountain region with blue and purple spikes marking residential concentrations in Nova Friburgo, Teresópolis and surroundings — In the mountain region, urban cores rise as peaks of residential density. Source: IBGE / CNEFE.

Street-level visualization of downtown Nova Friburgo, with 3D columns over each residential address — At street level: columns over every address in downtown Nova Friburgo. Source: IBGE / CNEFE.

Where companies are born

Heat map of new company registrations in Nova Friburgo, with the highest intensity downtown — New company registrations in Nova Friburgo. Source: CNPJ registry, Federal Revenue.

04 · Garment factories and the pandemic

Every CNPJ registration carries the economic activity code (CNAE), the opening date and, when applicable, the closing date. Filtering the active garment factories of Nova Friburgo's lingerie hub, we can map the sector's geography — and watch, record by record, the impact of the pandemic.

The distribution of garment factories

Map of the distribution of active garment factories in Nova Friburgo, mapped from company registrations — Active garment factories, mapped from CNPJ registrations. Source: CNPJ registry, Federal Revenue.

Opened and closed during the pandemic

Which sectors closed after others?

Ordering company closures over time reveals a sequence: some economic sectors closed first, others held on longer — a temporal trace of the crisis moving through the local economy.

Visualization comparing the temporal sequence of closures across different economic sectors during the pandemic — The order of closures by economic sector during the pandemic. Source: CNPJ registry, Federal Revenue.

05 · Ownership networks

The CNPJ registry publishes the ownership structure of every company. Linking partners to companies — and companies to other partners — turns economic power relations into a navigable graph: who stands next to whom, and through what.

Nova Friburgo businesspeople with the most companies

Graph of ownership relations linking the Nova Friburgo businesspeople with the largest number of companies — Ownership network of the Nova Friburgo businesspeople with the most companies. Source: CNPJ ownership records.

The companies of the "Tiger Game" owner

Graph of the companies and partners linked to the owner of the 'Tiger Game' gambling platform — Companies and partners of the "Jogo do Tigrinho" owner. Source: CNPJ ownership records. Click to open the interactive graph.

The business network of a senator

Graph of the partnerships and companies linked to a Brazilian senator — Partnerships and companies of a particular senator. Source: CNPJ ownership records.

Swiss companies headquartered in Brazil

06 · Wage Atlas of Brazil

The Annual Report of Social Information (RAIS) records every formal employment contract in the country. Aggregating those contracts by municipality, the atlas shows where wages concentrate — and where formal employment nearly vanishes from the map.

Choropleth map of Brazil with municipalities colored by the average wage of formal employment contracts in RAIS — Wages of formal employment contracts by municipality. Source: RAIS, Ministry of Labor and Employment, 2020–2024. Click to open the interactive atlas.

Explore the interactive atlas at ミ.xyz/dataviz/rais/mapa/.

07 · Housing censuses: 1970–2000

Each Sankey diagram reads an IBGE housing census in three layers: the household situation (rural, urban…), the occupancy condition (owned, rented, lent…) and the dwelling type. Following the flows across 1970, 1980 and 2000, the country's housing transformation appears all at once.

1970 Census

Housing patterns before mass urbanization: rural owned households still rival urban ones, and the predominant construction type is rustic.

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sankey-beta
"Urban","No declaration",1168
"Suburban","No declaration",26
"Rural","No declaration",775
"Urban","Owned (paid off)",6602220
"Suburban","Owned (paid off)",345236
"Rural","Owned (paid off)",6514120
"Urban","Owned (being acquired)",1021044
"Suburban","Owned (being acquired)",33202
"Rural","Owned (being acquired)",111461
"Urban","Rented",3575026
"Suburban","Rented",109313
"Rural","Rented",300507
"Unclassified (situation)","Rented",2
"Urban","Lent",906408
"Suburban","Lent",40742
"Rural","Lent",920018
"Urban","Other condition",139868
"Suburban","Other condition",13135
"Rural","Other condition",2859867
"Urban","Unclassified (condition)",16
"No declaration","Rustic",1434
"No declaration","Durable",535
"Owned (paid off)","Rustic",9912955
"Owned (paid off)","Durable",3548621
"Owned (being acquired)","Rustic",1032172
"Owned (being acquired)","Durable",133535
"Rented","Rustic",3465003
"Rented","Durable",519845
"Lent","Rustic",1235874
"Lent","Durable",631294
"Other condition","Rustic",1659069
"Other condition","Durable",1353801
"Unclassified (condition)","Rustic",16

Source: IBGE, 1970 Demographic Census — household situation → occupancy condition → construction type.

1980 Census

The great urbanization begins: cities and towns already concentrate most households, and apartment living starts to register in the statistics.

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sankey-beta
"Rural settlement","Rented",19572
"Rural settlement","Lent by employer",13165
"Rural settlement","Lent by individual",8836
"Rural settlement","Unknown",437
"Rural settlement","Other condition",3330
"Rural settlement","Owned (being acquired)",11155
"Rural settlement","Owned (paid off)",124728
"Isolated urban area","Rented",6098
"Isolated urban area","Lent by employer",1893
"Isolated urban area","Lent by individual",1910
"Isolated urban area","Unknown",38
"Isolated urban area","Other condition",342
"Isolated urban area","Owned (being acquired)",1212
"Isolated urban area","Owned (paid off)",15536
"City or town","Rented",1343492
"City or town","Lent by employer",96021
"City or town","Lent by individual",277264
"City or town","Unknown",6427
"City or town","Other condition",54910
"City or town","Owned (being acquired)",344477
"City or town","Owned (paid off)",2334531
"Rural zone","Rented",34671
"Rural zone","Lent by employer",429076
"Rural zone","Lent by individual",170478
"Rural zone","Unknown",3637
"Rural zone","Other condition",46914
"Rural zone","Owned (being acquired)",8230
"Rural zone","Owned (paid off)",1125161
"Rented","Apartment",192360
"Rented","House",1211473
"Lent by employer","Apartment",15897
"Lent by employer","House",524258
"Lent by individual","Apartment",15198
"Lent by individual","House",443290
"Unknown","Apartment",1420
"Unknown","House",9119
"Other condition","Apartment",3131
"Other condition","House",102365
"Owned (being acquired)","Apartment",107008
"Owned (being acquired)","House",258066
"Owned (paid off)","Apartment",115652
"Owned (paid off)","House",3484304

Source: IBGE, 1980 Demographic Census — household situation → occupancy condition → dwelling type.

2000 Census

The millennium turn: a simplified urban/rural classification, the consolidation of urban living and of fully-owned homes — with the apartment as an established category.

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sankey-beta
"Rural","Rented",67876
"Rural","Lent in another way",334512
"Rural","Lent by employer",776836
"Rural","Unknown",70761
"Rural","Other condition",68061
"Rural","Owned (being acquired)",78000
"Rural","Owned (paid off)",3476050
"Urban","Rented",2206025
"Urban","Lent in another way",855342
"Urban","Lent by employer",207996
"Urban","Unknown",108024
"Urban","Other condition",170945
"Urban","Owned (being acquired)",1146742
"Urban","Owned (paid off)",10707242
"Rented","Apartment",315423
"Rented","House",1907731
"Rented","Single room",50747
"Lent in another way","Apartment",35972
"Lent in another way","House",1124609
"Lent in another way","Single room",29273
"Lent by employer","Apartment",21095
"Lent by employer","House",956863
"Lent by employer","Single room",6874
"Unknown","Other",178785
"Other condition","Apartment",8251
"Other condition","House",222231
"Other condition","Single room",8524
"Owned (being acquired)","Apartment",287630
"Owned (being acquired)","House",933642
"Owned (being acquired)","Single room",3470
"Owned (paid off)","Apartment",604698
"Owned (paid off)","House",13508004
"Owned (paid off)","Single room",70590

Source: IBGE, 2000 Demographic Census — household situation → occupancy condition → dwelling type.

What the three censuses show

Accelerating urbanization: in 1970, rural households still rivaled urban ones; by 2000, urban areas concentrate roughly three times more households than rural ones.
Consolidation of home ownership: the fully-owned home is the dominant condition in all three censuses — and widens its lead over renting.
Access to credit: the "owned, being acquired" category grows markedly between 1970 and 2000, reflecting the expansion of housing finance.
The apartment era: nearly invisible in 1970, the apartment appears in 1980 and settles in as a relevant category by 2000.

🔗 Source repository: IBGE housing census analysis

About the data

We experiment with visualizing public data, integrating the IBGE Censuses, the CNEFE address registry and the Federal Revenue's company registry: population data by religious profile, education, race and age; companies with economic activities, ownership structures, religious organizations, police battalions, city halls, quilombola communities and banks — most of them geolocated — potentially cross-referenced with the Electoral Court (TSE), Chamber of Deputies expenses, parliamentary caucuses and "PIX" budget amendments.

Digital cartography makes it possible to spatialize complex knowledge — such as economic patterns, religious practices, or networks of power — that used to remain scattered or hidden in raw data. This spatialization turns large volumes of data into accessible cognitive interfaces, mobilizing both visual perception and critical analysis. (Drucker, 2011)