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Major release in PySAL/spreg v. 1.9.0 (github.com/pysal/spreg). This release introduces a fully refactored panels module that offers the following spatial panel functions (in addition to the SUR estimators already available). See examples in github.com/pysal/spreg/blob/…

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Analyzing how spatial processes change over time or across categories helps reveal dynamics in geographic systems. giddy is a Python library in the PySAL ecosystem that provides tools for spatial dynamics analysis, including spatial Markov chains and transition mapping. Useful when you want to: ✔️ Model spatial Markov chains to study state transitions ✔️ Visualize spatial dynamics and change patterns ✔️ Explore how spatial relationships evolve over time ✔️ Integrate temporal spatial analysis into Python workflows The image below shows examples from the documentation. Learn more here: pysal.org/giddy/ For ongoing insights on statistics, data science, R, and Python, subscribe to my newsletter. Check out this link for more details: statisticsglobe.com/newslett… #Python #StatisticalAnalysis #Python3 #statisticians #Data

Spatial data analysis relies on well-defined spatial relationships such as neighborhoods, distances, and connectivity structures. libpysal is a Python library that provides the core building blocks for spatial analysis, including spatial weights, geometric operations, and utilities used across the PySAL ecosystem. Well suited for: ✔️ Creating and analyzing spatial weights matrices ✔️ Defining neighborhood structures based on contiguity or distance ✔️ Preparing spatial inputs for spatial econometrics and modeling ✔️ Acting as a foundation for higher-level PySAL libraries The image below shows an example from the documentation. Learn more here: pysal.org/libpysal/ If you are interested in practical tools for statistics, data science, R, and Python, you can subscribe to my newsletter. More details are available at this link: statisticsglobe.com/newslett… #datasciencetraining #database #Python #DataAnalytics #Data #Python3

Measuring and comparing segregation patterns is a key task in urban studies, social science, and spatial analysis. segregation is a Python package that provides a comprehensive set of segregation indices and spatial decomposition tools as part of the PySAL ecosystem. Useful when you want to: ✔️ Compute classic and modern segregation indices ✔️ Analyze segregation across space and groups ✔️ Decompose segregation measures by subregions ✔️ Combine social data with spatial context in Python The image below shows examples from the documentation. Learn more here: pysal.org/segregation/ For more updates on statistics, data science, R, and Python, subscribe to my newsletter. Check out this link for more details: statisticsglobe.com/newslett… #RStats #datastructure #datasciencetraining #Rpackage

Spatial smoothing helps reduce noise and highlight underlying geographic patterns in data. tobler is a Python package in the PySAL ecosystem that provides tools for spatial smoothing and weighted aggregation, making it easier to refine spatial data for analysis and visualization. Useful when you want to: ✔️ Smooth rates or counts across geographic units ✔️ Highlight broader spatial patterns by reducing local noise ✔️ Prepare data for mapping and interpretation ✔️ Incorporate spatial weights into smoothing workflows in Python Below are selected visual examples from the documentation. Additional details are available here: pysal.org/tobler/ If you want regular updates on practical tools for statistics, data science, R, and Python, you can subscribe to my newsletter. Check out this link for more details: eepurl.com/gH6myT #RStats #datastructure #datasciencetraining #Rpackage

Analyzing economic and social inequality is a central topic in applied statistics, economics, and public policy. inequality is a Python package that provides tools to compute standard inequality measures such as the Gini, Theil, and Atkinson indices, with support for spatially structured data in the PySAL ecosystem. Useful when you want to: ✔️ Quantify inequality using well-established indices ✔️ Compare distributions across regions or population groups ✔️ Decompose inequality measures for deeper insights ✔️ Combine distributional analysis with spatial data in Python Below is a snapshot of figures from the documentation. More details are available here: pysal.org/inequality/ For more content on statistics, data science, R, and Python, subscribe to my newsletter. Further details: eepurl.com/gH6myT #datastructure #database #R #RStats #Statistical #Rpackage #coding

Exploratory spatial data analysis focuses on detecting spatial patterns, clustering, and spatial autocorrelation in geographic data. ESDA is a Python library that provides classic and local measures such as Moran’s I and Getis-Ord statistics to explore spatial structure within the PySAL ecosystem. Useful when you want to: ✔️ Detect global and local spatial autocorrelation ✔️ Identify spatial clusters and hotspots ✔️ Explore spatial patterns before formal modeling ✔️ Add spatial context to exploratory data analysis in Python The image below shows examples from the documentation. Learn more here: pysal.org/esda/ For more updates on statistics, data science, R, and Python, subscribe to my newsletter. More information: eepurl.com/gH6myT #StatisticalAnalysis #datastructure #RStats #DataAnalytics #R4DS #DataAnalytics

Spatial decision-making and optimization with geographic data is a common task in applied data science and planning. spopt is a Python library that provides optimization methods for location allocation, regionalization, and related spatial analyses within the PySAL ecosystem. Perfect for: ✔️ Facility location and service coverage analysis ✔️ Spatial regionalization and zoning tasks ✔️ Urban planning, transportation, and public policy applications ✔️ Combining spatial data with optimization techniques The image below shows some examples from the documentation. Learn more here: pysal.org/spopt/ For more on statistics, data science, R, and Python, subscribe to my newsletter. Further details: eepurl.com/gH6myT #Data #datasciencetraining #DataAnalytics #R #DataAnalytics #RStats

Spatial accessibility analysis examines how easily people can reach services such as healthcare, education, or retail across space. access is a Python package that implements widely used accessibility measures, including gravity-based models and two-step floating catchment area methods, within the PySAL ecosystem. Useful when you want to: ✔️ Assess access to services across regions ✔️ Compare accessibility between population groups ✔️ Study the effects of facility locations and travel costs ✔️ Add accessibility metrics to spatial analysis workflows in Python A preview of selected figures from the documentation is shown below. Additional information is available here: pysal.org/access/ I regularly share tools and examples on statistics, data science, R, and Python in my newsletter. Check out this link for more details: eepurl.com/gH6myT #Python #datastructure #datasciencetraining #Rpackage

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The algorithms themselves have a blazing-fast Rust backend, so you can solve and iterate on problems quickly. And they are adapted from seminal papers in the field and the PySAL group's cutting-edge research in spatial optimization.

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8️⃣ Serious spatial stats with PySAL Moran’s I, spatial weights, clustering, regression. Because pretty maps need solid inference. youtube.com/watch?v=i9TRi7r3…