Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-08-26 00:37 UTC

ipea / geobr

Easy access to official spatial data sets of Brazil in R and Python

R · PythonNo license detected★ 940 stars⑂ 129 forkssince Mar 2019View on GitHub ↗

ipea/geobr holds a health index of 81 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (81/100) and lowest on AI Readiness (56/100). It was last updated 4 days ago. A single contributor accounts for most of its recent work.

81
overall / 100
Excellent

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

81
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 69 is calibrated to 81 on the published index scale (record calibration 2026-08-02).

Ownership

91 followers51 public repossince Sep 2016

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIgeobr1.0.018,1341753 days ago

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

75Good · 21% of overall
How it's scored
36/36Push recencylast push 4 days ago
11.1/36Commit cadence16/52 weeks with commits
18/18Commit volume176 commits in the last year
10/10OpenSSF Scorecard: Maintained30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year176
human_commit_share0.93
days_since_last_push4
active_weeks_last_year16
How it's scored
27/27Ships releases23 releases published
36/36Release recencylatest release 63 days ago
12.6/27Release cadencea release every ~181.3 days
0/10OpenSSF Scorecard: Signed-ReleasesProject has not signed or included provenance with any releases.
Inputs used
releases_count23
latest_release_tagv2.0.1
releases_from_tagsno
days_since_latest_release63
mean_days_between_releases181.3

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

63Moderate · 17% of overall
How it's scored
48.2/60Stars940 stars
17.6/25Forks129 forks
8/15Watchers28 watchers
Inputs used
forks129
stars940
watchers28
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
0/22.5Licenseno license file detected
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseno
readme_badges7
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesbadge.fury.io, codecov.io, github.com, shields.io
has_pull_request_templateno
How it's scored
56.8/80Monthly downloads18,134 downloads/month across pypi
0/20Registry dependentsnot reported by this ecosystem
Inputs used
packagesgeobr
dependents
ecosystemspypi
total_downloads
monthly_downloads18,134
unverified_packages_excluded
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

67Good · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
3.3/22.5Commit distributiontop contributor authored 85% of commits
13.5/13.5Contributor breadth27 contributors
10/10OpenSSF Scorecard: Contributorsproject has 18 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled27
top_contributor_share0.854
How it's scored
39.7/42Issue resolution94% of issues closed
24.3/30PR acceptance81/100 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-ReviewFound 0/20 approved changesets -- score normalized to 0
Inputs used
merged_prs81
open_issues19
closed_issues326
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.945
closed_unmerged_prs19
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domain
14.1/25Owner reach91 followers of ipea
24.5/25Track record51 public repos, account ~9 yr old
Inputs used
followers91
owner_typeOrganization
is_verifiedno
owner_loginipea
public_repos51
account_age_days3,624

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 53 days ago
20/20Version history17 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesgeobr
ecosystemspypi
any_deprecatedno
min_days_since_publish53

Engineering Quality

Are baseline engineering and documentation practices in place?

81Excellent · 19% of overall
How it's scored
24/24CI workflows7 workflow(s)
24/24Tests present
16/16Linter configpython-package/pyproject.toml ([tool.black])
0/9.6Pre-commit hooks
0/6.4.editorconfig
4/20OpenSSF Scorecard: CI-Tests2 out of 8 merged PRs checked by a CI test -- score normalized to 2
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://ipea.github.io/geobr/
10/10Repository description
10/10Topics10 topics
10/10Wiki
Inputs used
topicsr, rstats, python, spatial-data, geopackage, shapefile, sf, geopandas, brazil, datasets
has_wikiyes
homepagehttps://ipea.github.io/geobr/
docs_sitehttps://ipea.github.io/geobr/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

57Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionbranch protection not enabled on development/release branches
0.5/2.5CI-Tests2 out of 8 merged PRs checked by a CI test -- score normalized to 2
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0/7.5Code-ReviewFound 0/20 approved changesets -- score normalized to 0
2.5/2.5Contributorsproject has 18 contributing companies or organizations
10/10Dangerous-Workflowno dangerous workflow patterns detected
7.5/7.5Dependency-Update-Toolupdate tool detected
0/5Fuzzingproject is not fuzzed
0/2.5Licenselicense file not detected
7.5/7.5Maintained30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
5/5Packagingpackaging workflow detected
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0/5SASTSAST tool is not run on all commits -- score normalized to 0
0/5Security-Policysecurity policy file not detected
0/7.5Signed-ReleasesProject has not signed or included provenance with any releases.
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated18
scorecard_versionv5.5.0
checks_inconclusive0
scorecard_aggregate4.6

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages20
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:geobr@1.0.0 runtime dependency closure — what installing the published package pulls in — 20 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

56Moderate · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
15/15Machine-readable docs (llms.txt)llms.txt served by the project website (https://ipea.github.io/geobr/llms.txt)
13.2/40Legible commit history23 of 93 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://ipea.github.io/geobr/llms.txt
legible_history_share0.247
agent_instruction_files
agent_instruction_max_bytes
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format configpython-package/pyproject.toml ([tool.black])
0/11Static type checking
10/10Reproducible environmentlockfile
10/10Demonstrated agent practice7 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance7 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0.07
toolchain_manifests
dependency_bot_commit_share0.07
How it's scored
0/45Type-checkable codeR without a type-check config
55/55Manageable file sizes0/109 source files over 60KB
Inputs used
primary_languageR
largest_source_bytes18,462
source_files_sampled109
oversized_source_files0
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesexamples, notebooks, samples
Inputs used
example_dirsexamples, notebooks, samples
has_mcp_signalno
api_schema_files
interfaces_expected_of
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

940GitHub stars
27contributors
176commits, last 12 months
4days since last push
23releases
1bus factor
19open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 129 ⇿
0Stars
129Forks
23Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

025507510012515012852019-062022-122026-06
Major 1Minor 4Patch 9

Each point covers 7 days.

OpenSSF Scorecard 4.6 / 10
4.6aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-08-26 00:36 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
2CI-Tests2 out of 8 merged PRs checked by a CI test -- score normalized to 2
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/20 approved changesets -- score normalized to 0
10Contributorsproject has 18 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
0Licenselicense file not detected
10Maintained30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 9
RegistryPackageVersion constraintManifest
PyPIgeopandas>=1.0.0,<=1.1.2python-package/pyproject.toml
PyPIshapely>=1.7.0,<=2.1.0python-package/pyproject.toml
PyPIrequests<3.0.0,>=2.25.1python-package/pyproject.toml
PyPIurllib3>=1.26.0,<3.0.0python-package/pyproject.toml
PyPIlxml>=5.1.0,<7.0.0python-package/pyproject.toml
PyPIhtml5lib==1.1python-package/pyproject.toml
PyPIpyarrow>=15.0.0python-package/pyproject.toml
PyPIduckdb>=1.5.3python-package/pyproject.toml
PyPIrapidfuzz>=3.0python-package/pyproject.toml
All dependencies 37

Full resolved dependency set from the GitHub dependency graph: 9 direct and 28 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIduckdb1.5.3direct
PyPIgeopandas1.1.2direct
PyPIhtml5lib1.1direct
PyPIlxml6.1.0direct
PyPIpyarrow24.0.0direct
PyPIrapidfuzz3.14.5direct
PyPIrequests2.33.0direct
PyPIshapely2.0.6direct
PyPIurllib32.7.0direct
PyPIcertifi2024.8.30indirect
PyPIcharset-normalizer3.4.0indirect
PyPIcolorama0.4.6indirect
PyPIexceptiongroup1.3.1indirect
PyPIexecnet2.1.1indirect
PyPIfire0.7.0indirect
PyPIgeobr1.0.0indirect
PyPIidna3.15indirect
PyPIiniconfig2.0.0indirect
PyPIjinja23.1.6indirect
PyPImarkupsafe3.0.2indirect
PyPInumpy2.0.2indirect
PyPIpackaging24.1indirect
PyPIpandas2.2.3indirect
PyPIpluggy1.5.0indirect
PyPIpygments2.20.0indirect
PyPIpyogrio0.10.0indirect
PyPIpyproj3.6.1indirect
PyPIpytest9.0.3indirect
PyPIpytest-xdist3.5.0indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpytz2024.2indirect
PyPIsix1.16.0indirect
PyPItermcolor2.5.0indirect
PyPItomli2.4.1indirect
PyPItyping-extensions4.15.0indirect
PyPItzdata2024.2indirect
PyPIwebencodings0.5.1indirect
Dependency advisories 0

Installing pypi:geobr@1.0.0 pulls in 20 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.