Modeling Strategies for Categorical Data: Examples from Housing and Tenure Choice

Models to investigate categorical data can be divided into preprocessing, limited parameterization, and formal logit models. To illustrate the advantages of preprocessing and limited parameterization models they are applied to a data set of tenure and type of housing choice before the data are examined with hierarchical logit and nested logit models. The preprocessing approaches are useful in selecting optimal subsets of independent variables with respect to the dependent variable. The ease of application and interpretation of a limited parameterization approach extends the clarity of the results from the preprocessing approaches. Because some variables are only relevant at specific levels of other independent variables, nonstandard (nested) logit models are necessary to understand the nested relationships.     

人口数据格网化模型研究进展综述

人口数据格网化是目前人口空间分布研究的热点。本文对国内外人口数据格网化模型研究进行总结,重点剖析几种主要的人口数据格网化模型。研究表明:人口数据格网化模型从单纯的、静态的格网化方法,逐步向自然、经济社会因素综合影响下的空间模型过渡,朝着动态模型方向发展;随着格网化数据应用领域的不断拓展,以不同级别格网为基本统计单元的人口数据生产将是人口数据获取的一个重要发展趋势。     

Reducing Errors in Rainfall Estimates through Rain Gauge Location

Facility location models are examined as a framework for generating rain gauge networks designed to reduce errors in mean areal precipitation (MAP) estimation. Errors in estimating MAP may be divided into two types: (i) capture error, not observing a storm which occurs in a gauged area, and (ii) extrapolation error, using a rain gauge measurement to represent a heterogeneous area. In this paper, five rain gauge location models are developed to minimize these errors. The models include adaptations of the maximal covering location problem, the p-median model, and three models derived from multicriteria cluster analysis. The models are tested using precipitation data from an experimental watershed maintained by the U.S. Department of Agriculture in Arizona. Analysis of the results reveals, for the particular watershed, that (1) in sparse networks, location of rain gauges can play a larger role than number of rain gauges in reducing errors in MAP estimates; (2) models based on mean hydrologic data provide nearly as good networks as models based on spatially correlated data; and (3) models yielding the best networks for estimating precipitation for flood predictions are different from the models providing the best precipitation estimates for low flow forecasts.     

GIS and the application of a model of pollen deposition and dispersal: a new approach to testing landscape hypotheses using the POLLANDCAL models

The recent advances in computer-based modelling of pollen dispersal and deposition through the POLLANDCAL (POLlen-LANDscape CALibration) network have enabled landscape simulations to be constructed and tested, both to validate the theoretical models, and to validate modelled landscapes using empirical data. This paper presents the first attempt to develop simulations based on real landscapes, by placing distinct vegetation communities using reclassified digital elevation models (combined slope and altitude data). The approach is tested by modelling a palynological signature for the expansion of mixed agriculture into an upland margin, and the results for 49 simulated pollen deposition sites, across an altitudinal range for a series of landscape scenarios, is compared with existing empirical data. The broad trends within the two data sets are in general accordance, and variation between them is attributed to local variation in vegetation heterogeneity. The results show that the POLLANDCAL models offer a new and exciting method by which sub-fossil pollen assemblages can be examined, theoretical models of landscape use tested, and will offer a significant tool in the interpretation of environmental data in archaeology, in particular when combined with GIS-based models of the physical environment.     

A Wavelet‐Based Extension of Generalized Linear Models to Remove the Effect of Spatial Autocorrelation. 基于小波扩展广义线性模型消除空间自相关的影响

Biogeographical studies are often based on a statistical analysis of data sampled in a spatial context. However, in many cases standard analyses such as regression models violate the assumption of independently and identically distributed errors. In this article, we show that the theory of wavelets provides a method to remove autocorrelation in generalized linear models (GLMs). Autocorrelation can be described by smooth wavelet coefficients at small scales. Therefore, data can be decomposed into uncorrelated and correlated parts. Using an appropriate linear transformation, we are able to extend GLMs to autocorrelated data. We illustrate our new method, called the wavelet‐revised model (WRM), by applying it to multiple regression with response variables conforming to various distributions. Results are presented for simulated data and real biogeographical data (species counts of the plant genus Utricularia [bladderworts] in grid cells throughout Germany). The results of our WRM are compared with those of GLMs and models based on generalized estimating equations. We recommend WRMs, especially as a method that allows for spatial nonstationarity. The technique developed for lattice data is applicable without any prior knowledge of the real autocorrelation structure.     

A CONDITIONALLY PARAMETRIC PROBIT MODEL OF MICRODATA LAND USE IN CHICAGO

Spatial data sets pose challenges for discrete choice models because the data are unlikely to be independently and identically distributed. A conditionally parametric spatial probit model is amenable to very large data sets while imposing far less structure on the data than conventional parametric models. We illustrate the approach using data on 474,170 individual lots in the City of Chicago. The results suggest that simple functional forms are not appropriate for explaining the spatial variation in residential land use across the entire city.     

New Estimates of Gravitational Attraction by Linear Programming

This paper uses data for intercity air-passenger travel to derive a set of general propulsion and attraction factors for twenty-five large U.S. cities. The paper develops a new way of examining the linkages between flows and nodal attraction. The technique is to assume that the flow data fit a simple gravity model, and then linear programming is used to maximize the consistency of the endogenous propulsion and attraction with the flow data. Various linear models are used to determine the pattern of urban attraction factors that are consistent with the gravitational flows. Linear programming as well as goal programming models are used to develop a set of analyses linking flows to urban hierarchies. Suggestions for further extensions of the method to address substantive issues of urban systems analysis are also provided.     

Quantifying Interpolation Errors in Urban Airborne Laser Scanning Models

Airborne laser scanning (ALS) is becoming an increasingly popular data capture technique for a variety of applications in urban surface modeling. Raw ALS data are captured and supplied as a 3D point cloud. Many applications require that these data are interpolated onto a regular grid in order that they may be processed. In this article, we identify and analyze the magnitudes and spatial patterning of residuals from ALS models of urban surfaces, at a range of different scales. Previous research has demonstrated the effects of interpolation method and scale upon the nature of error in digital surface models (DSMs), but the size and spatial patterning of such errors have not hitherto been investigated for urban surfaces. The contribution of this analysis is thus to investigate the ways in which different methods may introduce error, and to understand the uncertainty that characterizes urban surface models that are devised for a wide range of applications. The importance of the research is shown using examples of how the different methods may introduce different amounts of error and how the uncertainty information may benefit users of ALS height models. Our analysis uses a range of validation techniques, including split-sample, cross-validation, and jackknifing, to estimate the error created in DSMs of urban areas.     

3D model of the Can Sadurní cave

Graphic and metric information about the site and its environment and about the different structures and artefacts located are indispensable for the optimal management of an archaeological excavation. A Geographic Information System (GIS) proves useful in managing and analysing information and data obtained from different sources. Two techniques of data capture, Terrestrial Laser Scanning (TLS) and close range photogrammetry, are used to generate accurate and high-resolution 3D models of a cave, the silos and a combustion structure at the Can Sadurní site (Begues, NE Spain). This site is especially noteworthy on account of its complete stratigraphy of the Catalonian Holocene. The capture and processing of the photogrammetric and TLS data used to generate 3D models are discussed in detail. These models cannot be obtained by classic archaeological techniques since the record is made layer by layer, approximately one decimetre apart, with the result that the information between the layers is lost. Our findings highlight the potential of these techniques for obtaining accurate and photorealistic models that facilitate a complete analysis of the available information at an archaeological site.     

Multiple Contexts as Cross-classified Models: The Labor Vote in the British General Election of 1992

Voters make their decisions in social and geographical contexts that can be seen as different levels in an overall data structure. Increasingly these structures are being analyzed by multilevel models, but this approach has so far been limited to structures that are strictly hierarchical. This paper outlines the approach of cross-classified multilevel models in which units at lower levels in the structure can be nested in more than one higher-level unit simultaneously. An appropriate modeling framework is outlined, models are specified, and particular attention is paid to efficient computation. The approach is illustrated through a cross-classified logit analysis of Labor versus Conservative support for a nationally representative sample of voting behavior for the 1992 British General Election. The data is structured so that individual voters at level 1 are nested within constituencies at level 2 which are cross-classified by geographical and functional regionalizations at level 3. A conclusion discusses the general utility of a cross-classified approach to geographically based contextual research, while two technical appendices provide details on model estimation.     

Quantitative basin modeling: present state and future developments towards predictability

A critique review of the state of quantitative basin modeling is presented. Over the last 15 years, a number of models are proposed to advance our understanding of basin evolution. However, as of present, most basin models are two dimensional (2‐D) and subject to significant simplifications such as depth‐ or effective stress‐dependent porosity, no stress calculations, isotropic fracture permeability, etc. In this paper, promising areas for future development are identified. The use of extensive data sets to calibrate basin models requires a comprehensive reaction, transport, mechanical (RTM) model in order to generate the synthetic response. An automated approach to integrate comprehensive basin modeling and seismic, well‐log and other type of data is suggested. The approach takes advantage of comprehensive RTM basin modeling to complete an algorithm based on information theory that places basin modeling on a rigorous foundation. Incompleteness in a model can self‐consistently be compensated for by an increase in the amount of observed data used. The method can be used to calibrate the transport, mechanical, or other laws underlying the model. As the procedure is fully automated, the predictions can be continuously updated as new observed data become available. Finally, the procedure makes it possible to augment the model itself as new processes are added in a way that is dictated by the available data. In summary, the automated data/model integration places basin simulation in a novel context of informatics that allows for data to be used to minimize and assess risk in the prediction of reservoir location and characteristics.     

Modeling Interaction in a System of Markets

This paper develops a nested family of market interaction models for the general case of spatial or aspatial goods. Market structure effects are identified in the form of intraregional and extraregional competition. The competing destinations and competing central place models are shown to be special cases of the general modeling framework. An empirical example, using survey data collected from Chicago-area Management and Public Relations (SIC 874) firms, illustrates how the family of models may be used to identify market structure.     

A Model to Relate the Size of the Central Business District to the Population of a City

A set of dynamic models of CBD growth are developed. They hypothesize that growth rates depend upon the population growth rate together with the increasing unwillingness of people to travel to the city center as city size increases. A prediction of the model is that absolute decreases in CBD size can occur associated simply with population growth rather than technological change as is usually postulated. The models are tested using retail sales data for the United States. Good predictions are generally found, especially when the unique CBD of New York City is removed from the data set. However, the model which has the greatest theoretical validity relative to central place theory does not have the best fit to the data. Implications of this are discussed, along with those of utilizing transformations in regression models.     

INTERPRETING SUBJECTIVE DATA FOR PROGRAM EVALUATION

This paper examines how to interpret subjective data from citizen and client surveys for purposes of evaluating governmental programs. Six alternative models of the subjective response process are identified, and the implications of the different models are considered. Finally, different strategies for the statistical analysis of subjective data are discussed and illustrated.     

Models and Metadata: The Ethics of Sharing Bioarchaeological 3D Models Online

Digital 3D modelling is increasingly common in archaeological recording, but building the models is only part of the story. Sharing and interacting with these digital representations is essential in fostering engagement and ensuring that research is relevant to the broader community. Projects and institutions understand that much of their communication and outreach now take place via digital platforms. However, archaeologists are faced with a variety of challenges when sharing 3D models of human remains online, including metadata curation, accessibility, openness, copyright as well as the ethical implications of sharing digital bioarchaeological data and long-term storage requirements. In this research, metadata from a collection of 3D models of human remains were extracted from SketchFab and analysed to understand how users share 3D models of human remains via online social platforms. The results were contrasted with a critical review of current ethical and technical guidelines, indicating potentially ethically compromising practices, particularly the lack of contextualising metadata for some models. This article proposes and discusses recommendations for developing more strategic approaches to the sharing of 3D data on human remains online, such as assessing and agreeing hosting, licensing and metadata management during project design. Frameworks are provided to support these approaches and make decisions on how to share data openly.     

An information theoretic approach to the estimation of migration flows

This study is concerned with the application of different types of models to the study of migration flows. Two traditional approaches to migration modeling, the demographic fixed-rate and the gravity/entropy families of models, are first described, and their adequacy is assessed. The author next discusses the derivation of minimum information models. Consideration is given to destination-weighted models and to net-constrained models. A unified approach that incorporates both a priori micro and estimated macro parameters is then considered. The author concludes that the extended minimum information approach is the most suitable for building dynamic spatial interaction models, given the current state of U.S. internal migration data.     

Validation of Multiclass Urban Travel Forecasting Models Combining Origin–Destination, Mode, and Route Choices

Abstract The formulation, estimation, and validation of combined models for making detailed urban travel forecasts are described. These models combine origin–destination, mode, and auto route choices into a consistent forecasting method for multiple user classes for the Chicago Region. Household Travel Survey and Census Transportation Planning Package data for 1990, respectively, are used to estimate and validate the model.     

Logistic Population Growth in the World's Largest Cities

This article demonstrates that recent population growth in the world's largest cities has conformed to the general parameters of the logistic process. Using data recently provided by the United Nations, logistic population growth for 485 million-person cities is analyzed at 5-year intervals during 1950–2010, with the UN projections for 2015 adopted as upper limits. A series of ordinary least-squares regression models of increasing complexity are estimated on the pooled data. In one class of models, the logarithms of population proportions are specified to be linear in time, which is the standard approach, but in a second class of models those proportions are specified as being quadratic. The most complex models control logistic growth estimates for (i) city-specific effects (e.g., initial population), (ii) nation-specific effects (e.g., economic development, age distribution of population), and (iii) global coordinates (for unobserved effects). Moreover, the results are segregated according to each city's membership in four different growth clubs, which was an important finding of previous research.     

Stable state conditions of population-dependent migration models under a zero natural growth rate

The author "attempts to examine the stable conditions of regional population under a zero natural growth rate in the context of a certain general class of [population-dependent] nonlinear migration models." Theorems regarding the stable state conditions of the migration models are presented. The parameters of the gravity migration model are then estimated empirically using data on Japanese inter-prefectural migration flows in 1966, 1970, and 1975. The possibility of achieving the stable state in Japan is discussed