git: 176436adbfd6 - main - graphics/py-geosnap: Add py-geosnap 0.14.0
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Date: Sun, 04 Aug 2024 17:21:47 UTC
The branch main has been updated by sunpoet:
URL: https://cgit.FreeBSD.org/ports/commit/?id=176436adbfd6df0f67926945995abd207364762f
commit 176436adbfd6df0f67926945995abd207364762f
Author: Po-Chuan Hsieh <sunpoet@FreeBSD.org>
AuthorDate: 2024-08-04 16:44:09 +0000
Commit: Po-Chuan Hsieh <sunpoet@FreeBSD.org>
CommitDate: 2024-08-04 17:13:52 +0000
graphics/py-geosnap: Add py-geosnap 0.14.0
geosnap provides a suite of tools for exploring, modeling, and visualizing the
social context and spatial extent of neighborhoods and regions over time. It
brings together state-of-the-art techniques from geodemographics,
regionalization, spatial data science, and segregation analysis to support
social science research, public policy analysis, and urban planning. It provides
a simple interface tailored to formal analysis of spatiotemporal urban data.
Main Features:
- fast, efficient tooling for standardizing data from multiple time periods into
a shared geographic representation appropriate for spatiotemporal analysis
- analytical methods for understanding sociospatial structure in neighborhoods,
cities, and regions, using unsupervised ML from scikit-learn and spatial
optimization from PySAL
- classic and spatial analytic methods for diagnosing model fit, and locating
(spatial) statistical outliers novel techniques for understanding the
evolution of neighborhoods over time, including identifying hotspots of local
neighborhood change, as well as modeling and simulating neighborhood
conditions into the future
- quick access to a large database of commonly-used neighborhood indicators from
U.S. providers including Census, EPA, LEHD, NCES, and NLCD, streamed from the
cloud thanks to quilt and the highly-performant geoparquet file format.
---
graphics/Makefile | 1 +
graphics/py-geosnap/Makefile | 48 +++++++++++++++++++++++++++++++++++++++++++
graphics/py-geosnap/distinfo | 3 +++
graphics/py-geosnap/pkg-descr | 21 +++++++++++++++++++
4 files changed, 73 insertions(+)
diff --git a/graphics/Makefile b/graphics/Makefile
index 3dbe0b7c506a..1450edc57d10 100644
--- a/graphics/Makefile
+++ b/graphics/Makefile
@@ -901,6 +901,7 @@
SUBDIR += py-gdal
SUBDIR += py-geomdl
SUBDIR += py-geopandas
+ SUBDIR += py-geosnap
SUBDIR += py-giddy
SUBDIR += py-gizeh
SUBDIR += py-glcontext
diff --git a/graphics/py-geosnap/Makefile b/graphics/py-geosnap/Makefile
new file mode 100644
index 000000000000..38f9269bbb9f
--- /dev/null
+++ b/graphics/py-geosnap/Makefile
@@ -0,0 +1,48 @@
+PORTNAME= geosnap
+PORTVERSION= 0.14.0
+CATEGORIES= graphics python
+MASTER_SITES= PYPI
+PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX}
+
+MAINTAINER= sunpoet@FreeBSD.org
+COMMENT= Geospatial Neighborhood Analysis Package
+WWW= https://oturns.github.io/geosnap-guide/ \
+ https://github.com/oturns/geosnap
+
+LICENSE= BSD3CLAUSE
+LICENSE_FILE= ${WRKSRC}/LICENSE.txt
+
+BUILD_DEPENDS= ${PYTHON_PKGNAMEPREFIX}setuptools>=61.0:devel/py-setuptools@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}setuptools-scm>=6.2:devel/py-setuptools-scm@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}wheel>=0:devel/py-wheel@${PY_FLAVOR}
+RUN_DEPENDS= ${PYTHON_PKGNAMEPREFIX}contextily>=0:graphics/py-contextily@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}fsspec>=:devel/py-fsspec@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}geopandas>=0.9:graphics/py-geopandas@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}giddy>=2.2.1:graphics/py-giddy@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}libpysal>=0:science/py-libpysal@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}mapclassify>=0:graphics/py-mapclassify@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}matplotlib>=0:math/py-matplotlib@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}numpy>=0,1:math/py-numpy@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}pandana>=0:graphics/py-pandana@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}pandas>=0,1:math/py-pandas@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}platformdirs>=0:devel/py-platformdirs@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}pooch>=0:devel/py-pooch@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}proplot>=0.9:graphics/py-proplot@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}pyarrow>=0.14.1:databases/py-pyarrow@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}pyproj>=3:graphics/py-pyproj@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}quilt3>=3.6:www/py-quilt3@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}s3fs>=0:devel/py-s3fs@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}scikit-learn>=0:science/py-scikit-learn@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}seaborn>=0:math/py-seaborn@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}segregation>=2.1:science/py-segregation@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}spopt>=0.3.0:math/py-spopt@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}tobler>=0.8.2:science/py-tobler@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}tqdm>=0:misc/py-tqdm@${PY_FLAVOR} \
+ ${PYTHON_PKGNAMEPREFIX}xlrd>=0:textproc/py-xlrd@${PY_FLAVOR}
+
+USES= python
+USE_PYTHON= autoplist concurrent pep517
+
+NO_ARCH= yes
+
+.include <bsd.port.mk>
diff --git a/graphics/py-geosnap/distinfo b/graphics/py-geosnap/distinfo
new file mode 100644
index 000000000000..301a31ce5e9c
--- /dev/null
+++ b/graphics/py-geosnap/distinfo
@@ -0,0 +1,3 @@
+TIMESTAMP = 1722711129
+SHA256 (geosnap-0.14.0.tar.gz) = 3ef75ef510934e5eb30b0e9c80cd94952a1266a05a5987ae06945781e97eac1f
+SIZE (geosnap-0.14.0.tar.gz) = 29913517
diff --git a/graphics/py-geosnap/pkg-descr b/graphics/py-geosnap/pkg-descr
new file mode 100644
index 000000000000..9ddb78464622
--- /dev/null
+++ b/graphics/py-geosnap/pkg-descr
@@ -0,0 +1,21 @@
+geosnap provides a suite of tools for exploring, modeling, and visualizing the
+social context and spatial extent of neighborhoods and regions over time. It
+brings together state-of-the-art techniques from geodemographics,
+regionalization, spatial data science, and segregation analysis to support
+social science research, public policy analysis, and urban planning. It provides
+a simple interface tailored to formal analysis of spatiotemporal urban data.
+
+Main Features:
+- fast, efficient tooling for standardizing data from multiple time periods into
+ a shared geographic representation appropriate for spatiotemporal analysis
+- analytical methods for understanding sociospatial structure in neighborhoods,
+ cities, and regions, using unsupervised ML from scikit-learn and spatial
+ optimization from PySAL
+- classic and spatial analytic methods for diagnosing model fit, and locating
+ (spatial) statistical outliers novel techniques for understanding the
+ evolution of neighborhoods over time, including identifying hotspots of local
+ neighborhood change, as well as modeling and simulating neighborhood
+ conditions into the future
+- quick access to a large database of commonly-used neighborhood indicators from
+ U.S. providers including Census, EPA, LEHD, NCES, and NLCD, streamed from the
+ cloud thanks to quilt and the highly-performant geoparquet file format.