A SIMPLE METHOD FOR ESTIMATING QUARTERLY LOCAL-AREA PERSONAL INCOME*

ABSTRACT A single-equation econometric approach is developed for estimating personal income on a quarterly basis for counties and county aggregates (e.g., metropolitan areas). An experiment is conducted on state data to test the accuracy of the estimates. The experiment indicates that they would be highly accurate for large local areas; for local areas comprising at least 20 percent of their state's total personal income, the estimates would fall within 2 percent of BEA-type estimates 95 percent of the time. An aggregate estimation approach is shown to be preferable to a component approach. Using data on local department store sales, the quarterly personal income estimates are shown to forecast better than the currently available annual estimates.     

On the Spatial Decomposition of Forecasts

This paper uses historical socioeconomic data to evaluate the elasticities of a Dendrinos‐Sonis one‐population/two‐locations nonlinear dynamic comparative advantage model for determining subregional shares of aggregate regional forecasts. The fractal dimension properties of the discrepancies between the actual and simulated data are used to enhance the forecasting framework. The analysis focuses in the first phase on total population, total personal income, and earnings by sector for the Columbus, Ohio Metropolitan Statistical Area, and Delaware County, one of its component counties. For the second phase of the analysis, these data for nondurable goods and for services are used, along with monthly data for total employment in Columbus, Cincinnati, and Cleveland, Ohio. Annual data for the analysis are drawn from the U. S. Bureau of Economic Analysis, Regional Economic Information System, while the monthly data come from the Ohio Bureau of Employment Securities. The nonlinear dynamic model is shown to outperform conventional approaches for the majority of socioeconomic stocks.     

UN Population Division's Methodology in Preparing Base Population for Projections: Case study for India

Preparation of the base population plays a substantial role in the accuracy of projections. This paper reviews analytical strategies involved in the preparation of the UN World Population Prospects to reconcile existing discrepancies between data sources and derive robust estimates of population counts, distribution, and demographic changes. The case study of India is used to illustrate the challenges and benefits of using a cohort-component population projection approach in reconstructing its population between 1950 and 2010, including the preparation of bias-free population counts and distributions by age and sex; estimation of consistent fertility and mortality trends; and integration of all demographic components for obtaining a reliable base population for projections.     

Demand-Threshold Estimation for Business Activities in Rural Saskatchewan

Historically, a common technique used to assess a community's ability to support various business activities has been demand-threshold estimation. The concept of a demand threshold has been applied at the community level by estimating a relationship between community population and the number of establishments of a particular kind in the community. The approach taken in this research incorporates a spatial dimension (community urban proximity) in addition to community population to estimate demand thresholds. Using a count data technique, demand thresholds are estimated for 27 different business activities found in 584 rural Saskatchewan communities in 1990. Both population and urban proximity are found to be important explanatory variables.     

A Geostatistical Linear Regression Model for Small Area Data. 一种适用于小区域数据的地统计线性回归模型

We present a new linear regression model for use with aggregated, small area data that are spatially autocorrelated. Because these data are aggregates of individual‐level data, we choose to model the spatial autocorrelation using a geostatistical model specified at the scale of the individual. The autocovariance of observed small area data is determined via the natural aggregation over the population. Unlike lattice‐based autoregressive approaches, the geostatistical approach is invariant to the scale of data aggregation. We establish that this geostatistical approach also is a valid autoregressive model; thus, we call this approach the geostatistical autoregressive (GAR) model. An asymptotically consistent and efficient maximum likelihood estimator is derived for the GAR model. Finite sample evidence from simulation experiments demonstrates the relative efficiency properties of the GAR model. Furthermore, while aggregation results in less efficient estimates than disaggregated data, the GAR model provides the most efficient estimates from the data that are available. These results suggest that the GAR model should be considered as part of a spatial analyst's toolbox when aggregated, small area data are analyzed. More important, we believe that the GAR model's attention to the individual‐level scale allows for a more flexible and theory‐informed specification than the existing autoregressive approaches based on an area‐level spatial weights matrix. Because many spatial process models, both in geography and in other disciplines, are specified at the individual level, we hope that the GAR covariance specification will provide a vehicle for a better informed and more interdisciplinary use of spatial regression models with area‐aggregated data.     

Bayesian Estimation of Regional Production for CGE Modeling

Abstract Computable general equilibrium (CGE) models are often criticized for using restrictive functional forms and relying on external sources for parameter values in their calibration. CGE modelers argue that in many instances reliable econometric estimates of important model parameters are unavailable because they must be estimated using small numbers of time‐series observations. To address these criticisms, this paper uses a Bayesian approach to estimate the parameters of a translog production function in a regional computable general equilibrium model. Using priors from more reliable national estimates, and parameter restrictions required by neoclassical production theory, estimation is done by Markov chain Monte Carlo simulation. A stylized regional CGE model is then used to contrast policy responses of a Cobb‐Douglas specification with those from the estimated translog equation.     

Serbian Spatial Planning among Styles of Spatial Planning in Europe

The classification of spatial planning styles in Europe has been successively reviewed a few times. Most recently, it has been done in the framework of the European Spatial Planning Observatories Network (ESPON) 2.3.2 project dealing with the territorial governance. Based on national overviews for 29 European countries, this project uses the classification of spatial planning systems in four styles: comprehensive-integrated, regional-economic, land-use and urbanism. This study did not take into account the spatial planning system in Serbia since it is neither in the European Union nor a member of the ESPON similar to Norway and Switzerland. This article uses the form of national overviews elaborated for the ESPON project and puts Serbia in a comparative spatial planning perspective, classifying it between the comprehensive-integrated and land-use planning styles.     

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.     

A Spatiotemporal Bayesian Hierarchical Approach to Investigating Patterns of Confidence in the Police at the Neighborhood Level

Public confidence in the police is crucial to effective policing. Improving understanding of public confidence at the local level will better enable the police to conduct proactive confidence interventions to meet the concerns of local communities. Conventional approaches do not consider that public confidence varies across geographic space as well as in time. Neighborhood level approaches to modeling public confidence in the police are hampered by the small number problem and the resulting instability in the estimates and uncertainty in the results. This research illustrates a spatiotemporal Bayesian approach for estimating and forecasting public confidence at the neighborhood level and we use it to examine trends in public confidence in the police in London, UK, for Q2 2006 to Q3 2013. Our approach overcomes the limitations of the small number problem and specifically, we investigate the effect of the spatiotemporal representation structure chosen on the estimates of public confidence produced. We then investigate the use of the model for forecasting by producing one‐step ahead forecasts of the final third of the time series. The results are compared with the forecasts from traditional time‐series forecasting methods like naïve, exponential smoothing, ARIMA, STARIMA, and others. A model with spatially structured and unstructured random effects as well as a normally distributed spatiotemporal interaction term was the most parsimonious and produced the most realistic estimates. It also provided the best forecasts at the London‐wide, Borough, and neighborhood level.     

Heterogeneity of Attribute Sampling Error in Spatial Data Sets

This paper considers the standard error of the estimate of the mean of a spatially correlated variable in the case where data are obtained by a process of random sampling. Two distinct mean estimation problems are identified: estimating the area mean and estimating the population mean. Methods are described for obtaining standard error estimates in the two cases and, within the limits of publicly available information, the methods are implemented on average household income data at the census tract scale for Syracuse, New York. The purpose of the paper is to draw attention to issues of data precision in relation to sampled geographic information on averages and in particular to consider the problems of estimating standard errors using such data. The paper also examines the extent to which standard errors vary between census tracts.     

Panel regression formulas for estimating stature and body mass from immature human skeletons: a statistical approach without reference to specific age estimates

Anthropologists require methods for accurately estimating stature and body mass from the human skeleton. Age-structured, generalized Least Squares (LS) regression formulas have been developed to predict stature from femoral length and to predict body mass in immature human remains using the width of the distal metaphysis, midshaft femoral geometry (J), and femoral head diameter. This paper tests the hypothesis that panel regression is an appropriate statistical method for regression modeling of longitudinal growth data, with longitudinal and cross-sectional effects on variance. Reference data were derived from the Denver Growth Study; panel regression was used to create one formula for estimating stature (for individuals 0.5–11.5 years old); two formulas for estimating body mass from the femur in infants and children (0.5–12.5 years old); and one formula for estimating body mass from the femoral head in older subadults (7–17.5 years old). The formulas were applied to an independent target sample of cadavers from Franklin County, Ohio and a large sample of immature individuals from diverse global populations. Results indicate panel regression formulas accurately estimate stature and body mass in immature skeletons, without reference to an independent estimate for age at death. Thus, using panel regression formulas to estimate stature and body mass in forensic and archaeological specimens may reduce second stage errors associated with inaccurate age estimates.     

Monitoring the Near‐surface Urban Heat Island in Beijing,China by Satellite Remote Sensing

Urban heat island (UHI) effect is an important impact factor of the regional climate and ecological environment. How to observe and analyse the spatial distribution of UHI has become an important issue of urban environmental research. In this paper, the near‐surface air temperature of Beijing was derived based on the Landsat/TM satellite imagery on 26 July 2011 to study the near‐surface UHI. A statistical model at 195‐m window size was established to estimate the air temperature, using land surface temperature, normalized difference vegetation index, altitude, and surface albedo as independent variables. The mean absolute error (MAE) of the model was 0.87°C, and the R² was 0.66, indicating that the method can be used to effectively estimate the air temperature. The air temperature distribution obtained from remote sensing revealed that the UHI effect in Beijing was very significant and showed a concentrated pattern. The heat island intensity was stronger in the southern part than in the northern part of the city. In addition, the relationship between the air temperature and impervious surfaces was analysed. The air temperature increased with increasing impervious surface coverage, and the rate of change depended on the impervious surface coverage. When the impervious surface coverage was below 40 per cent, the temperature increased rapidly with increasing impervious surface coverage, and when the impervious surface coverage was above 40 per cent, the temperature increased slowly. This study provides a new approach to monitor near‐surface UHI and reveals its relationship with impervious surface, providing a scientific reference for urban planning and environmental assessment.     

Don't throw out the baby with the bathwater: Estimating fertility from subadult skeletons

Recent research interest has focused on the bioarchaeology of children. Although paleodemography is essential for accurate reconstructions of lifestyle and health in past populations, currently there is no published technique for estimating fertility and life expectancy at birth for skeletal populations in which adults are under‐enumerated. This paper provides a formula to predict Gross Reproductive Rate (GRR) from the proportion of young infants to subadults in a skeletal population. The formula was developed from 98 of Coale and Demeny's Female Model West Life Tables, which represented diverse fertility and mortality rates. The formula's accuracy was examined using independent samples from historical and archaeological cemeteries. Estimates of GRR from the subadult fertility formula were compared with estimates from Bocquet‐Appel and Masset's juvenile:adult ratio. Results indicate that the subadult fertility formula predicts GRR with consistent accuracy (R² = 0.98) and precision (± 1 offspring) in the model life tables, across diverse subadult age structures and demographic characteristics. The formula is useful for subadult populations with a proportion of perinates:subadults between 0.12 and 0.45. The adult component of the sample is not included in the analysis and thus the formula is similarly useful in cases where adults are under‐enumerated, or not. When applied to historical and archaeological populations, estimates for GRR are similar to previous estimates from the juvenile:adult ratio. Because crude birth rate and life expectancy at birth can be calculated from GRR using established fertility centred approaches to demography, the subadult fertility formula allows skeletal populations of diverse composition to be included in demographic research, essential for understanding of how mortality and fertility are affecting the morbidity profiles of subadult samples and for comparative bioarchaeological analyses. Copyright © 2010 John Wiley & Sons, Ltd.     

A General Misspecification Test for Spatial Regression Models: Dependence, Heterogeneity, and Nonlinearity

There is an increasing awareness of the potentials of nonlinear modeling in regional science. This can be explained partly by the recognition of the limitations of conventional equilibrium models in complex situations, and also by the easy availability and accessibility of sophisticated computational techniques. Among the class of nonlinear models, dynamic variants based on, for example, chaos theory stand out as an interesting approach. However, the operational significance of such approaches is still rather limited and a rigorous statistical-econometric treatment of nonlinear dynamic modeling experiments is lacking. Against this background this paper is concerned with a methodological and empirical analysis of a general misspecification test for spatial regression models that is expected to have power against nonlinearity, spatial dependence, and heteroskedasticity. The paper seeks to break new research ground by linking the classical diagnostic tools developed in spatial econometrics to a misspecification test derived directly from chaos theory—the BDS test, developed by Brock, Dechert, and Scheinkman (1987). A spatial variant of the BDS test is introduced and applied in the context of two examples of spatial process models, one of which is concerned with the spatial distribution of regional investments in The Netherlands, the other with spatial crime patterns in Columbus, Ohio.     

Estimating a Destination-Choice Model from a Choice-based Sample with Limited Information

In most applications of multinomial logit and other probabilistic discrete-choice models, the estimation data set is either a simple random sample of the population of interest or an exogenously stratified sample. Often, however, it is cheaper and easier to sample individuals while they are carrying out the chosen activity of concern. This produces a choice-based sample, which presents important problems of estimation and inference. This paper is concerned with estimation of destination-choice models from choice-based samples when neither the aggregate market shares of alternatives nor the probability distribution of explanatory variables in the population is known. The method of Cosslett (1981) for estimating multinomial logit models from such data is summarized, and the limitations on information about choice behavior that can be recovered from the sample are explained. An empirical model of pharmacy choice in the Namur, Belgium, area is presented. It is shown that useful and important information about destination-choice behavior can be obtained from a choice-based sample, even without knowledge of aggregate market shares and the probability distribution of explanatory variables.     

Identifying the Impacts of Land-Use Spatial Patterns on Street-Network Accessibility Using Geospatial Methods

While the land use-street network nexus is well acknowledged, evidence for the one-way impacts of land-use patterns on street accessibility is still inadequate. The measurements of land-use patterns and street accessibility lack systematic knowledge. Their empirical correlations also lack geographical variability, constraining site-specific land-use practices. Therefore, this study overcame the aforementioned limitations by examining the two-level spatial models to formulate accessibility-oriented land plans, using a well-developed Chinese city as an example. Firstly, two landscape metrics—Euclidean Nearest-Neighbor Distance (ENN) and Similarity Index (SIMI)—were used to quantify the intra- and inter-land-use configurations, respectively. Both city-level and local accessibility were measured using spatial design network analysis. Performing both ordinary least squares (OLS) and geographically weighted regression (GWR) models, results identified the statistically significant effects of inter-land-use patterns on two-level street accessibility. An exception was that land-use configurations within residential and industrial regions were irrelevant to street accessibility. We also found GWR was a better-fitting model than OLS when estimating locally-varied accessibility, suggesting hierarchical multiscale land-use planning. Overall, locally heterogeneous evidence in this study can substantialize land use-street network interactions and support the decision-making and implementation of place-specific accessibility-oriented land use.     

Sample-specific (universal) metric approaches for determining the sex of immature human skeletal remains using permanent tooth dimensions

In this paper, three approaches for developing sample-specific sex determination methods of immature skeletal remains based on permanent tooth dimensions are proposed and tested using a sample of identified skeletons. The sample comprises adult and subadult individuals selected from the Lisbon documented skeletal collection, housed at the National Museum of Natural History in Lisbon, Portugal. Faciolingual and mesiodistal diameters were the tooth dimensions utilized. In the first approach, sex-specific logistic regression formulae based on adult tooth dimensions are developed and then used to determine the sex of the subadult sample. The second and third approaches are based on the sectioning point procedure, which uses the overall mean of a measurement (tooth diameter) collected from the sample as the discriminant criteria for determining the sex of the individuals in that sample. While in the second approach the adult overall mean of each dimension is used as the discriminant criteria for determining the sex of the subadults, in the third approach it is the subadult overall mean of each dimension that comprises the discriminant criteria that is applied back to the subadults for determining sex. Results show that the canines are the teeth with the highest sexual dimorphism and methods of sex determination based on canine dimensions provide correct allocation accuracies between 58.8% and 100% depending on the diameter and the approach that is being used. Canine faciolingual dimensions provide the best overall results. Combinations of measurements from the same and different teeth do not increase significantly the accuracy of the methods and approaches. Some of the problems of subadult sex determination methods based on adult tooth dimensions result from differing levels of sexual dimorphism between the adult and subadult segment of the sample. Mortality or cultural bias may increase or decrease the sexual dimorphism of the subadults compared to the adults. Small subadult samples utilized in this study may also raise questions regarding the accuracy of the three different sample-specific approaches. However, high consistency of results using the canine and different approaches, suggests that adult and subadult canine dimensions can be reliable sex discriminators of immature skeletal remains in archaeological samples. The major advantage of the approaches presented here is that they can be used to derive sample-specific methods and, therefore, eliminate the problem of applying morphological or metric methods to individuals originating from a population that differs from the one that contributed to the development of the method.     

Regional income inequality in France 1860–1954: Methods and findings

This paper explores regional (département or NUTS3) income inequality in France between 1860 and 1954. To this end we first document the existing evidence, evaluate the estimation methods and findings, assess the suitability of each approach and address potential concerns. We then present our own subnational estimates of per-capita income, derived following Geary and Stark. Overall, we find that indirect estimation (or top-down) methods provide somewhat differing results, thereby calling into question the validity of some of these approaches. Our estimates, meanwhile, appear to fit reasonably well with direct estimations (or bottom-up) of value-added and income and provide evidence in support of a decline in regional inequality over the period of study.