Announcing new book series

Great news! We’re announcing a new book series with Springer Science and Business Media B.V. on Spatial Demography. Jeremy Porter and I will be Book Series Editors for significant works involving spatial demography. This follows our current forthcoming work on the edited volume, Recapturing Space: New Middle-Range Theory In Spatial Demography with Stephen A. Matthews (Penn State), to further discourse in this area of study. I’ve posted the official Springer Science and Business Media B.V. narrative about the Series that will appear in the near future on their website.

If you or your colleagues have ideas for potential volumes in this Series, please drop us a note at editors@spatialdemography.org. We’d love to hear from you!

Frank

Spatial Demography Series

This series explores the application of spatial science to demographic information, including the methods, theory, and applications to societal problems. It captures the rapidly expanding knowledge-base of how human behavior and social processes are both shaped by place and time as well as change localities themselves over time.

The books in this series examine both individuals as well as institutions and include all methodological, geographical, and topical research or treatments of the interaction between societies and place. It encompasses racial segregation, crime, urban sprawl, agricultural production, engagement in community life, migration, commuting, business location, technology, environmental quality, elections to public office, and other important societal phenomena.

This series adopts an international and interdisciplinary approach in its detail of the tools, techniques, and theoretical perspectives used in spatial demography. The individual volumes will help demographers better understand when, how, and why space matters in social behavior and institutions.

This entry was posted by Frank Howell on September 23, 2013 in Forum

New Springer book on neighborhoods

I just saw this new book from Springer:

Neighbourhood Effects or Neighbourhood Based Problems?
A Policy Context

Editors: David Manley, Maarten van Ham, Nick Bailey, Ludi Simpson, Duncan Maclennan
ISBN: 978-94-007-6694-5 (Print) 978-94-007-6695-2 (Online)

http://link.springer.com/book/10.1007/978-94-007-6695-2/page/1

Many of you may be able to get the whole thing free from Springerlink from your respective libraries. It has a lot of stuff on crime, but also health and lots on policy-relevant research, and most of the work is from the UK.

Related to the book review by Stephen Matthews on linked micromaps (Spatial Demography  2013 1(1):141-143), readers might be interested to know that there are two R packages now available to produce linked micromaps.  Several colleagues and I at the EPA worked together to develop the micromap package.  In that package, we describe how to produce linked micromaps for a variety of areal units, such as states, counties, districts, watersheds, etc.  Dan Carr, Jim Pearson, and Linda Pickle developed the package micromapST to produce linked micromaps specifically for the 50 U.S. states and the District of Columbia.  Both packages are available at the Comprehensive R Archive Network (CRAN:  http://cran.rstudio.com/ ).

PySAL 1.6 Officially Released

See the following announcement from one of our editorial board members (Serge Rey) highlighting the official release of PySal 1.6.

PySAL is a library of tools for spatial data analysis and
geocomputation written in Python. PySAL 1.6, the seventh official
release of PySAL brings the following key enhancements:

### Spatial weights (weights)

* Optimized contiguity builder
* Explicit checks for disconnected observations (islands)
* Lightweight sparse weights class
* Handle coincident points in construction of distance based weights
* Optimized construction of knn weights

### Spatial regression (spreg)

* Chow test on spatial autoregressive coefficient in error, lag and combination regime models
* Kernel based weights specialized for HAC estimators
* Group-wise heteroskedasticity correction for OLS models with regimes
* Optimal GMM estimator to account for heteroskedasticity in TSLS models with regimes

### Spatial inequality (inequality)

* Spatial decomposition of the Gini coefficient

### Computational geometry (cg)

* Robust segment intersection tests

among the 246 commits and bug fixes since the last release, 6 months ago.

In addition, 1.6 marks the first release since PySAL moved from Google Code to GitHub

## PySAL modules

* pysal.core — Core Data Structures and IO
* pysal.cg — Computational Geometry
* pysal.esda — Exploratory Spatial Data Analysis
* pysal.inequality — Spatial Inequality Analysis
* pysal.spatial_dynamics — Spatial Dynamics
* pysal.spreg – Regression and Diagnostics
* pysal.region — Spatially Constrained Clustering
* pysal.weights — Spatial Weights
* pysal.FileIO — PySAL FileIO: Module for reading and writing various file types in a Pythonic way

## Downloads

Source distributions are available at
http://pypi.python.org/pypi/PySAL

Binary installers are availble from the [GeoDa Center for Geospatial Analysis and Computation](https://geodacenter.asu.edu/projects/pysal)

PySAL can also be installed with pip or easy_install.

## Documentation
The documentation site is here
http://pythonhosted.org/PySAL/

## Web sites
PySAL’s home is here
http://pysal.org/

The developer’s site is here
https://github.com/pysal/pysal

## Mailing Lists
Please see the developer’s list here
http://groups.google.com/group/pysal-dev

Help for users is here
http://groups.google.com/group/openspace-list

## Bug reports and feature requests
To search for or report bugs, as well as request enhancements, please see
https://github.com/pysal/pysal/issues

## License information
See the file “LICENSE.txt” for information on the history of this
software, terms & conditions for usage, and a DISCLAIMER OF ALL
WARRANTIES.

Many thanks to [all who contributed!](https://github.com/pysal/pysal/blob/master/THANKS.txt)

New Bayesian spatial model paper

One of our Editors, Corey Sparks, has just published with coauthors a new paper using Bayesian models…we hope he will include a future Software & Code column on some of these methods!

P. Johnelle Sparks, Corey S. Sparks, Joseph J. A. Campbell. “An application of Bayesian spatial statistical methods to the study of racial and poverty segregation and infant mortality rates in the US.” GeoJournal April 2013, Volume 78, Issue 2, pp 389-405.

Abstract

The infant mortality rate is a fundamental measure of population health used internationally. In the United States, the infant mortality rate is higher than what would be expected for a country of its affluence. We present an analysis of US county infant mortality rates using modern Bayesian spatial statistical methodologies. Our key predictors in our statistical analysis are residential racial and poverty segregation, measured by the dissimilarity, interaction and spatial proximity indexes. We use both Exploratory Spatial Data Analysis methods and Hierarchical Bayesian spatial regression models to examine the influences of these segregation measures on the infant mortality rate for each county, net of income inequality, degree of rurality and relative socioeconomic deprivation. The spatial measures of racial segregation suggest that when blacks live in close proximity to each other, this tends to increase the infant mortality rate. The results for poverty segregation suggest the same pattern, when poor populations live in close proximity to one another this is generally detrimental to the county infant mortality rate. However, interaction between blacks and whites and poor and non-poor residents of an area is protective for infant mortality.

Study on “place matters” in upward mobility

Has anyone been carefully following the study making the current news cycle, The Economic Impacts of Tax Expenditures: Evidence from Spatial Variation Across the U.S. (http://obs.rc.fas.harvard.edu/chetty/tax_expenditure_soi_whitepaper.pdf)? See http://www.nytimes.com/2013/07/22/business/in-climbing-income-ladder-location-matters.html. Also, an interview with one of the authors at: http://inplainsight.nbcnews.com/_news/2013/07/23/19620629-key-to-climbing-out-of-poverty-location-location-location?lite

Tolbert et al. Commuting Zones (CZs)

Tolbert et al. Commuting Zones (CZs)

I’ve been fielding calls from local reporters about this study…which I’ve quickly read…but I’m unclear about their SOI database. They only cite economists on the general concern with how “place” shapes intergenerational mobility (!!) but say that they compare “where” the child was located prior to age 13 and after 25 with parents income. All these incomes are essentially ranks of parental and child incomes using a rank correlation as the measure of mobility. Svalagostoga propagated the term “permeability” to reflect such measures but there are of course many others since his time.

Any thoughts by anyone on this study?

Frank

 

 

This entry was posted by Frank Howell on July 25, 2013 in Forum

Essential Python Geospatial Libraries

I recently posted something on my other blog about Essential Python Geospatial Libraries, and thought that it might also be useful to the Spatial Demography community. The following is a list of Python packages that I (and others) regularly use for handling, exploring, analyzing, and generally mucking about with spatial data.

  • pandas – For data handling and munging. This is an extremely powerful tool for working with data in a spreadsheet-like format. If you’re familiar with R data.frames, then you’ll love pandas.
  • shapely – For geometry handling. This is the de facto  package for geometry handling and manipulation.
  • cartopy – For plotting spatial data. This is a pretty new package, and is actively being developed. It makes for really nice publication quality maps already, and plays really nicely with shapely geometries and matplotlib (see below).
  • rtree – For efficiently querying spatial data. This is a relatively simple spatial index package that really speeds up spatial bounding box queries.
  • nodebox-opengl – For playing around with animations. Everyone loves animations, and this package makes it pretty easy to create some simple, interactive animations.
  • statsmodels – For models and stats in Python. The group behind this project is trying to make Python just as easy and powerful for stats as R currently is… and they are making excellent headway already.
  • numpy – For pretty much anything that involves arrays. The is probably the most important package for data analysis in Python.
  • geopy – For geolocating addresses and things like that. It is a small wrapper around various web-based geocoding APIs.
  • ipython – For a wonderful interactive Python environment in which to play. It makes working with Python a true joy.
  • ogr/gdal – For reading, writing, and transforming geospatial data formats. This has all sorts of nice ways to work with geospatial data, though fiona (see below) is much nicer for reading and writing different geospatial formats.
  • pyqgis – For anything and everything GIS. This is the Python API for Quantum GIS. You can work directly within QGIS via its intergrated Python console, or create standalone GIS apps using this powerful Python package.
  • fiona – For making it easy to read/write geospatial data formats. Really, really nice API for reading and writing GIS formats.
  • matplotlib – For all your plotting needs. The de facto plotting library for Python. It does everything from scatterplots and histograms to choropleth maps to complex interactive visualizations.
  • networkx – For working with networks. Very nice for visualizing (integrates with matplotlib) and working with network data.
  • pysal – For all your spatial econometrics needs (and more). This is a really great package that is also under constant development. It covers everything from exploratory spatial data analysis (ESDA) right up to heavy duty spatial econometric models.
  • descartes – For plotting geometries in matplotlib. This is a nice, clean, GeoJSON style data helper for matplotlib and shapely.
  • geographiclib – For solving geodesic problems. It also converts between geographic, UTM, UPS, MGRS, geocentric, and local cartesian coordinates, and even does geomagnetic field calculations.
  • pyshp – For reading and writing shapefiles in pure Python. Nice API and works great.
  • pyproj – For conversions between projections. This package provides an intuitive way of interacting with the Proj4 library for transforming data between coordinate reference systems.

It is also probably a good idea to mention scipy, which is a Python-based ecosystem of open-source software, and includes things like numpy, matplotlib, ipython, pandas, and much more.

The best way to start exploring and working with these various packages is to create a Python virtual environment, and use pip or easy_install to grab the latest versions.

Happy coding!

FOAS Affiliation

Spatial Demography is now affiliated with the Foundation for Open Access Statistics. It’s led by the prominent statistician Jan de Leuuw at UCLA. Located at www.foastat.org, the mission statement is reproduced below. We felt that the mission of Spatial Demography is compatible to that of FOAS and they have listed their support of this journal on their website. Give it a look!

Frank

The mission of the Foundation for Open Access Statistics (FOAS) is to promote free software, open access publishing, and reproducible research in statistics.

FOAS works to ensure the continued success of the Journal of Statistical Software (JSS), one of the few major open access journals that is free for both readers and authors. We also promote the use and development of free software for statistics, such as the R language and environment for computational statistics. We encourage members and the academic community at large to publish reproducible research that is publicly available online, e.g., in an open access journal or on an open access pre-print server.

You can join FOAS to show your support for free statistical software, open access publishing, and reproducible research in statistics. Membership is free and open to all.

This entry was posted by Frank Howell on July 7, 2013 in Uncategorized

Nice review of “community” concepts, approaches

Earlier I had posted on the issues raised in the Santa Barbara (UCSB) meeting of spatial demography specialists regarding ecological vs. grounded approaches to the social community concept. The current issue of Rural Sociology (78, 2, June) has a nice review of much literature from sociology by Peter Fairbrother et al. in “Creating ‘Community’: Preparing for Brushfire in Rural Victoria”. It’s worth reading just for this summary, IMHO.

New paper in Online First: Poverty Segregation in Nonmetro Counties: A Spatial Exploration of Segregation Patterns in the US

Poverty Segregation in Nonmetro Counties: A Spatial Exploration of Segregation Patterns in the US

By: Johnelle Sparks, Corey S. Sparks and Joseph J.A. Campbell

Abstract: Most research on segregation focuses on racial residential segregation in metropolitan statistical areas and typically uses a-spatial measures of segregation. What is less clear is if segregation measures operate in a similar fashion in nonmetropolitan areas and if spatial patterns exist for poverty segregation in nonmetro counties. The purpose of this research was to examine multiple dimensions of poverty segregation in the United States the period 2006-2010 for metropolitan and nonmetropolitan counties. Data for this analysis come from the 2006-2010 American Community Survey 5 year estimates, the 2000 U.S. Census of Population and Housing, Summary File 3 and the USDA Economic Research Service. Four different measures of poverty segregation were calculated, including both aspatial and spatial measures. A nonparametric Kruskal-Wallis test was used to test for variation in the segregation indices across metro and nonmetro areas and spatially autoregressive models were used to examine the socioeconomic correlates of poverty segregation. Results indicate significant variation in poverty segregation patterns in metro and nonmetro counties in the US, and nonmetro counties outside of the South have significantly lower levels of poverty segregation. This research adds to the literature by exploring patterns of metro and nonmetro poverty segregation and measuring different dimensions of segregation with an explicit spatial referent across counties in the contiguous United States in an effort to note differences in how segregation works across rural and urban places.