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

Segregation involves more than one population group, and segregation measures quantify how different population groups are distributed across space. One of the key conceptual and methodological foundations of segregation studies is to account for the potential of spatial interaction among two or more population groups across areal units. This foundation implies the need for a spatial approach to portray the spatial (and thus social) interaction among neighbors. In general, simple percentages (for example, percent Black) are not a measure of segregation. Because local spatial segregation measures did not emerge until recently, the objectives of this article are threefold: (1) to explain a spatial approach for measuring the level of segregation at the neighborhood (or local) level, (2) to demonstrate the deficiencies of using a percentage of racial/ethnic group as a measure of segregation, and (3) to clarify the appropriateness of two commonly used indexes of dissimilarity and diversity. Data from St. Louis, Missouri, and Chicago, Illinois, are used to discuss these three points.(ProQuest: ... denotes formulae omitted.)IntroductionResidential segregation and the persistence thereof have long been topics of interest to a wide variety of academic disciplines (for example, sociology, demography, geography political science, and public health) and to professionals or practitioners in multiple fields (for example, law enforcement, urban planning, and health service providers). Particularly in the United States, such phenomena have been viewed as a key factor of significant separation between White and Black residents.Therefore, formulating potential solutions to reduce the levels of residential segregation have been considered as a major societal concern (for example, Anderson et al., 2003; Charles, 2003; Clark, 1986; Massey and Fischer, 2000; Taeuber, 1968; Williams, 1999; Williams and Collins, 2001). Note that all racial groups in this article refer to the non-Hispanic populations.With a view to inform public policies and decisionmaking, however, the use of effective and meaningful segregation measures is fundamental and crucial to develop a reliable depiction and understanding of the social environment that different population groups experience in their place of residence (Johnston, Poulsen, and Forrest, 2014).1 Since the publication of the review papers (for example, Massey and Denton, 1988; Massey, White, and Phua, 1996) that assessed several dozens of segregation measures, many more segregation measures have been introduced. Many of these newer measures are extensions or modifications of existing measures (for example, Feitosa et al., 2007; Reardon and O'Sullivan, 2004; Wong, 2008, 2002), but some are actually not measures of segregation (for example, Brown and Chung, 2006; Reibel and Regelson, 2007). The mushrooming in the number of segregation measures reflects that the concept of segregation is fluid, difficult to pin down, and multifaceted so that one or a few simple definitions are not capable of capturing its essence entirely. As a result, rather ineffective and insufficient ways of measuring segregation are evident in research and practice.One major "malpractice" quite prevalent among studies focusing on neighborhood comparisons is using the percentage of racial and ethnic groups (for example, percent Black) as a measure of segregation to examine, for instance, the possible effects of residential segregation on academic performance (for example, Bennett, 2011; Card and Rothstein, 2007), home equity (for example, Deng, Ross, and Wachter, 2003; Kim, 2000), and health (for example, Inagami et al., 2006; Vinikoor et al., 2008). Census statistical units (tracts or block groups) have been used to denote the "neighborhoods" in most U.S. studies (including the six studies listed previously). Percentages, however, are not a measure of segregation (Johnston, Poulsen, and Forrest, 2007; Massey and Denton, 1988; Massey, White, and Phua, 1996; Reardon and O'Sullivan, 2004). …

Spatializing segregation measures: an approach to better depict social relationships

Quite often, geographical analysis involves comparing the spatial distributions of two variables or attributes. A typical method is to calculate the correlation coefficient of the two variables for corresponding areal units. Putting aside the fact that correlation coefficient is aspatial in nature (swapping attribute values between spatial units will not alter the value of correlation coefficient) and the issue of spatial dependency (or the potential existence of spatial autocorrelation) among observations, another major problem with using correlation measures for analyzing spatial data is the modifiable areal unit problem (MAUP), especially with the scale effect. Results from correlation analysis vary with the spatial resolutions based upon which spatial data are gathered. This paper presents an approach for spatial correlation analysis for count variables by comparing their cumulative spatial distributions. Using the concept of cumulative distribution function (CDF) in classical statistics, this paper shows that location-specific CDF (LSCDF) and its associated K-S-like statistic, which indicate the magnitude of difference between the two spatial distributions, are highly consistent over different levels of spatial scale. The application of the LSCDF approach is not restricted to isotropic spatial processes and the statistic provides a rather conservative conclusion. In addition, given any origin to construct LSCDFs, the LSCDFs can provide a geographic description of the two spatial distributions. By combining LSCDFs derived from different origins, a comprehensive understanding of the two distributions for the entire study area is developed. This approach for correlation analysis may offer a direction for future investigation of the MAUP.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1538-4632.2001.tb00438.x

Location-Specific Cumulative Distribution Function (LSCDF): An Alternative to Spatial Correlation Analysis

Many of the challenges of the next century will have physical dimensions, such as tsunamis, hurricanes, and climate change as well as human dimensions such as economic crises, epidemics, and emergency responses. With pioneering editors and expert contributors, Advanced Geoinformation Science explores how certain technical aspects of geoinformation have been used and could be used to address such global issues. The editors and chapter authors have been involved in global initiatives and research problems, such as Global Earth Observation System of Systems (GEOSS), Digital Earth, Air Quality, Public Health, and Cloud Computing. The book delineates the problems communities are likely to face and how advanced geoinformation science can be a part of their solution. It introduces different methods in collecting spatial data as the initial feeds to geoinformation science and computing platforms. It discusses systems for data management, data integration and analysis, the geoinformation infrastructure, as well as knowledge capture, formatting, and utilization. The book then explores a variety of geoinformation applications, highlighting environmental, agriculture, and urban planning uses. Geoinformation science encompasses more than just traditional technologies such as Remote Sensing, GIS, GPS, and supporting disciplines. And although the science continues to become more multidisciplinary, the literature remains compartmentalized according to the traditional disciplinary boundaries. Capturing recent developments in geoinformation science and linking IT with a wide range of Earth sciences, the authors explain how advanced technology and concepts play a significant role in recent advancements. Enhanced with forty-four color illustrations, live examples such as GOS and AirNow, and insights from NASA, EPA, and USGS, the book provides a vision for the future and explores how to bring that vision into reality.

Advanced Geoinformation Science

Computational fluid dynamics (CFD) models are powerful computational tools to simulate urban-landscape scale atmospheric dispersion events. They are proven to be very useful for security management and emergency response. Essential inputs to CFD models include landscape characteristics, which are often captured by various GIS data layers. While it is logical to couple GIS and CFD models to take advantage of available GIS data and the visualization and cartographic rendering capabilities of GIS, the integration of the two tools have been minimal. In this paper, we took the first step to evaluate the use of GIS data in CFD modeling. Specifically, we explore how efficient is to use GIS data in CFD models and how sensitive the CFD results are to different GIS data formats. Using campus topography and building data, and the FEFLO-URBAN CFD model, we performed atmospheric release simulations using topographic data in contour and raster formats. We found that using raster format was quite efficient and contour data required significant effort. Though the simulation outputs from the two data formats were not identical, their overall outcomes were similar and did not post alarming discrepancies. We concluded that using GIS data have tremendous potential for CFD modeling.

Integrating computational fluid dynamics (CFD) models with GIS: an evaluation on data conversion formats

Dear Editor: Nations around the world have been urging their citizens to practice “social distancing.” It has become the moniker for what we “need” to do to fight the COVID-19 pandemic. However, social distancing is a misnomer. It is not self-explanatory, conceptually ambiguous, practically misleading, and intellectually misplaced. A recent Oxford English Dictionary update reflects social distancing in the context it is in use currently—separating ourselves physically to avoid infection (Paton 2020). In public health, social distancing refers to community infection-control measures to achieve physical separation by restricting movements of and contacts between individuals during outbreaks (Fong et al. 2020). The US Centers for Disease Control and Prevention (2020) guideline for social distancing directs people to stay “at least 6 feet (2 meters) from others,” and “out of crowded places...” Thus, practices of social distancing are purely physical-spatial with nothing social in nature. In fact, “...social distance,... as an epidemiologic principle, has not been used in the exact terminology adapted by sociologists...” (Ipsen 1959, p. 162). The concept of social distance, which originated from Georg Simmel (Ethington 1997), has long been used in sociology, psychology, and geography to evaluate social separation among individuals and population groups. Measured on the Bogardus (1925) Social Distance Scale, it is interpreted as a function of an individual’s “prejudice” toward a/any sociological group. Thus, social distance is not the same as physical distance, although they can correlate (Forrest, La Grange, & Yip 2004). This distinction renders the current and past use of social distancing in public health problematic and out of sync with what the term actually means. Consequently, some authorities had provided confusing guidelines. For instance, England’s social distancing guideline instructed people to reduce “social interaction,” but also directed citizens to maintain social interaction using information-communication technologies (Public Health England 2020). A literal and intuitive interpretation of the social distancing message may indeed result in unintended social isolation. Similarly, advocating social distancing undermines the importance of “physical” separation essential for breaking the chain of infection to limit its spread. While some (cf. Government of Canada 2020) have transitioned away from the term, others continue to use it (cf. CDC 2020). Some have supported the continued use of social distancing because it is now a familiar phrase, and switching to a more physical term may no longer be needed (Gale 2020). Any such claim founded solely on the current context is shortsighted. The succession of zoonotic pandemics in the twenty-first century directs us to a longer-term outlook for better public health preparedness. Moreover, rapidly evolving global events require sharing of information across cultures and disciplines, but the misleading nature of the term inhibits this communication. Removing social distancing from the public health vocabulary and replacing it with a term such as physical distancing or separation would ensure unambiguous health messaging and avoid further academic confusion. We urge governments and public health officials to lead the change and appeal to the media to support correct public health messaging. No more “social distancing” but practice physical separation.

/pdf/no-more-social-distancing-but-practice-physical-separation-11lmieah7s.pdf

David Wong

Papers

Spatializing segregation measures: an approach to better depict social relationships

Location-Specific Cumulative Distribution Function (LSCDF): An Alternative to Spatial Correlation Analysis

Advanced Geoinformation Science

Integrating computational fluid dynamics (CFD) models with GIS: an evaluation on data conversion formats

No more "social distancing" but practice physical separation.