Fibroblasts can condense a hydrated collagen lattice to a tissue-like structure 1/28th the area of the starting gel in 24 hr. The rate of the process can be regulated by varying the protein content of the lattice, the cell number, or the con- centration of an inhibitor such as Colcemid. Fibroblasts of high population doubling level propagated in vitro, which have left the cell cycle, can carry out the contraction at least as efficiently as cycling cells. The potential uses of the system as an immu- nologically tolerated "tissue" for wound hea ing and as a model for studying fibroblast function are discussed.

Production of a tissue-like structure by contraction of collagen lattices by human fibroblasts of different proliferative

Introduction.- Data management and software.- Advice for teachers.- Exploration.- Linear regression.- Generalised linear modelling.- Additive and generalised additive modelling.- Introduction to mixed modelling.- Univariate tree models.- Measures of association.- Ordination--first encounter.- Principal component analysis and redundancy analysis.- Correspondence analysis and canonical correspondence analysis.- Introduction to discriminant analysis.- Principal coordinate analysis and non-metric multidimensional scaling.- Time series analysis--Introduction.- Common trends and sudden changes.- Analysis and modelling lattice data.- Spatially continuous data analysis and modelling.- Univariate methods to analyse abundance of decapod larvae.- Analysing presence and absence data for flatfish distribution in the Tagus estuary, Portugual.- Crop pollination by honeybees in an Argentinean pampas system using additive mixed modelling.- Investigating the effects of rice farming on aquatic birds with mixed modelling.- Classification trees and radar detection of birds for North Sea wind farms.- Fish stock identification through neural network analysis of parasite fauna.- Monitoring for change: using generalised least squares, nonmetric multidimensional scaling, and the Mantel test on western Montana grasslands.- Univariate and multivariate analysis applied on a Dutch sandy beach community.- Multivariate analyses of South-American zoobenthic species--spoilt for choice.- Principal component analysis applied to harbour porpoise fatty acid data.- Multivariate analysis of morphometric turtle data--size and shape.- Redundancy analysis and additive modelling applied on savanna tree data.- Canonical correspondence analysis of lowland pasture vegetation in the humid tropics of Mexico.- Estimating common trends in Portuguese fisheries landings.- Common trends in demersal communities on the Newfoundland-Labrador Shelf.- Sea level change and salt marshes in the Wadden Sea: a time series analysis.- Time series analysis of Hawaiian waterbirds.- Spatial modelling of forest community features in the Volzhsko-Kamsky reserve.

Analysing Ecological Data

This study aimed to review the literature describing and quantifying time lags in the health research translation process. Papers were included in the review if they quantified time lags in the development of health interventions. The study identified 23 papers. Few were comparable as different studies use different measures, of different things, at different time points. We concluded that the current state of knowledge of time lags is of limited use to those responsible for R&D and knowledge transfer who face difficulties in knowing what they should or can do to reduce time lags. This effectively ‘blindfolds’ investment decisions and risks wasting effort. The study concludes that understanding lags first requires agreeing models, definitions and measures, which can be applied in practice. A second task would be to develop a process by which to gather these data.

The Answer Is 17 Years, What Is the Question

In this paper we describe a cellular automaton (CA) simulation model developed to predict urban growth as part of a project for estimating the regional and broader impact of urbanization on the San Francisco Bay area's climate. The rules of the model are more complex than those of a typical CA and involve the use of multiple data sources, including topography, road networks, and existing settlement distributions, and their modification over time. In addition, the control parameters of the model are allowed to self-modify: that is, the CA adapts itself to the circumstances it generates, in particular, during periods of rapid growth or stagnation. In addition, the model was written to allow the accumulation of probabilistic estimates based on Monte Carlo methods. Calibration of the model has been accomplished by the use of historical maps to compare model predictions of urbanization, based solely upon the distribution in year 1900, with observed data for years 1940, 1954, 1962, 1974, and 1990. The complexity of this model has made calibration a particularly demanding step. Lessons learned about the methods, measures, and strategies developed to calibrate the model may be of use in other environmental modeling contexts. With the calibration complete, the model is being used to generate a set of future scenarios for the San Francisco Bay area along with their probabilities based on the Monte Carlo version of the model. Animated dynamic mapping of the simulations will be used to allow visualization of the impact of future urban growth.

/pdf/a-self-modifying-cellular-automaton-model-of-historical-4znon8yqbj.pdf

A Self-Modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay Area

Poor housing conditions are associated with a wide range of health conditions, including respiratory infections, asthma, lead poisoning, injuries, and mental health. Addressing housing issues offers public health practitioners an opportunity to address an important social determinant of health. Public health has long been involved in housing issues. In the 19th century, health officials targeted poor sanitation, crowding, and inadequate ventilation to reduce infectious diseases as well as fire hazards to decrease injuries. Today, public health departments can employ multiple strategies to improve housing, such as developing and enforcing housing guidelines and codes, implementing “Healthy Homes” programs to improve indoor environmental quality, assessing housing conditions, and advocating for healthy, affordable housing. Now is the time for public health to create healthier homes by confronting substandard housing.

Housing and Health: Time Again for Public Health Action

In this paper the examination of the modifiable areal unit problem is extended into multivariate statistical analysis. In an investigation of the parameter estimates from a multiple linear regression model and a multiple logit regression model, conclusions are drawn about the sensitivity of such estimates to variations in scale and zoning systems. The modifiable areal unit problem is shown to be essentially unpredictable in its intensity and effects in multivariate statistical analysis and is therefore a much greater problem than in univariate or bivariate analysis. The results of this analysis are rather depressing in that they provide strong evidence of the unreliability of any multivariate analysis undertaken with data from areal units. Given that such analyses can only be expected to increase with the imminent availability of new census data both in the United Kingdom and in the USA, and the current proliferation of GIS (geographical information system) technology which permits even more access to agg...

The Modifiable Areal Unit Problem in Multivariate Statistical Analysis

An adaptive inverse-distance weighting spatial interpolation technique

Even though Gehlke and Biehl (1934) discovered certain aspects of the modifiable areal unit problem (MAUP), the term MAUP was not coined formally until Openshaw and Taylor (1979) evaluated systematically the variability of correlation values when different boundaries systems were used in the analysis. The problem is called “the modifiable areal unit” because the boundaries of many geographical units are often demarcated artificially, and thus can be changed. For example, administrative boundaries, political districts, and census enumeration units are all subject to be redrawn. When data are gathered according to different boundary definitions, different data sets are generated. Analyzing these data sets will likely provide inconsistent results. This is the essence of the MAUP.

The Modifiable Areal Unit Problem (MAUP)

Functions to calculate measures of spatial association, especially measures of spatial autocorrelation, have been made available in many software applications Measures may be global, applying to the whole data set under consideration, or local, applying to each observation in the data set Methods of statistical inference may also be provided, but these will, like the measures themselves, depend on the support of the observations, chosen assumptions, and the way in which spatial association is represented; spatial weights are often used as a representational technique In addition, assumptions may be made about the underlying mean model, and about error distributions Different software implementations may choose to expose these choices to the analyst, but the sets of choices available may vary between these implementations, as may default settings This comparison will consider the implementations of global Moran’s I, Getis–Ord G and Geary’s C, local $$I_i$$
 and $$G_i$$
 , available in a range of software including Crimestat, GeoDa, ArcGIS, PySAL and R contributed packages

/pdf/comparing-implementations-of-global-and-local-indicators-of-17w0ryxrr4.pdf

Comparing implementations of global and local indicators of spatial association

1. Attribute Descriptors 1.1 Central Tendency 1.2 Dispersion and Distribution 1.3 Relationship 1.4 Trend 2. Point Descriptors 2.1 The Nature of Point Features 2.2 Central Tendency of Point Distributions 2.3 Dispersion of Point Distributions 2.4 Application Examples 2.4.1 References 3. Pattern Detectors 3.1 Scale, Extent, and Projection 3.2 Quadrat Analysis 3.3 Nearest Neighbor Analysis 3.4 Spatial Autocorrelation 3.5 Application Examples 3.5.1 References 4. Line Descriptors 4.1 The Nature of Linear Features 4.2 Characteristics and Attributes of Linear Features 4.3 Directional Statistics 4.4 Network Analysis 4.5 Application Examples 4.5.1 References 5. Pattern Descriptors 5.1 Spatial Relationships 5.2 Spatial Autocorrelation 5.3 Spatial Weights Matrices 5.4 Types of Spatial Autocorrelation Measures and Some Notations 5.5 Joint Count Statistics 5.6 Moran and Geary Indices 5.7 General G-Statistic 5.8 Local Spatial Autocorrelation Statistics 5.9 Moran Scatterplot 5.10 Application Examples 5.11 Summary 5.11.1 References Index

David Wong

Papers

The Modifiable Areal Unit Problem in Multivariate Statistical Analysis

An adaptive inverse-distance weighting spatial interpolation technique

The Modifiable Areal Unit Problem (MAUP)

Comparing implementations of global and local indicators of spatial association

Statistical Analysis with ArcView GIS