Probabilistic Regional Population Forecasts: The Example of Queensland, Australia

The variability of demographic trends at the subnational scale, particularly internal and international migration, renders subnational population forecasting more difficult than at the national scale. Illustrating the uncertainty of the demographic future for subnational regions is therefore a crucial element of any set of subnational population forecasts. However, subnational forecasts are currently prepared using deterministic models, which fail to properly address the issue of demographic uncertainty. The traditional high, medium, and low variants approach employed by many national statistical offices poses a number of problems. Probabilistic population forecasting models have the potential to overcome many of these problems, but these models have so far been limited to national-level forecasts. This article reports a first attempt to implement a probabilistic approach to subnational population forecasting using a biregional projection framework. The article sets out the forecasting framework, outlines the approach adopted to formulate each of the assumptions, and presents probabilistic forecasts for 2002–2051 for Queensland and the rest of Australia. The forecasts show a two-thirds probability that Queensland's population in 2051 will be between 5.4 and 7.7 million while the same range for the rest of the country is 18.6 and 22.7 million. The forecasts quantify to what extent greater uncertainty exists about the demographic future at the subnational compared with the national scale.     

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.     

An analysis of population and social change in London wards in the 1980s

"This paper discusses the estimation and projection of small area populations in London, [England] and considers trends in intercensal social and demographic indices which can be calculated using these estimates. Information available annually on vital statistics and electorates is combined with detailed data from the Census Small Area Statistics to derive demographic component based population estimates for London's electoral wards over five year periods. The availability of age disaggregated population estimates permits derivation of small area social indicators for intercensal years, for example, of unemployment and mortality. Trends in spatial inequality of such indicators during the 1980s are analysed and point to continuing wide differentials. A typology of population and social indicators gives an indication of the small area distribution of the recent population turnaround in inner London, and of its association with other social processes such as gentrification and ethnic concentration."     

Regional demographic trends in France: an examination at the level of the "zones d'emploi"

"The paper studies the regional demographic trends in France from 1968 to 1990.... The study follows two main objectives: (1) Analysis of the main regional migration and population trends over the three census periods from 1968 to 1990. (2) Analysis of the growth trends of different types of regions on the basis of typifying the ?zones d'emploi' (labour market regions) according to economic criteria and settlement structure criteria. The large-scale patterns of the population and migration trends seem to be relatively stable in comparison to the massive change in the general economic and political framework in France and Western Europe.... The different region types, however, show more distinct changes." (SUMMARY IN ENG AND FRE AND RUS)     

Spatial optimisation of fire service coverage: a case study of Brisbane,Australia

In the context of changing demand for fire services, spatial optimisation of fire coverage has attracted little scholarly attention despite its potential to improve emergency response and to inform future service planning for fire stations. Drawing on small area population forecasts, this paper extends the application of the Maximum Coverage Location Model to compute and delineate the spatial coverage of current and proposed new fire stations to align with population growth estimates for Brisbane, Australia. Our results reveal important gaps in fire cover that are likely to emerge as a result of predicted population growth, the spatial patterns of which varies across the Brisbane metropolitan area. We draw on these results to delineate a series of new potential sites for fire stations to ameliorate the reduction in spatial coverage as a consequence of predicted population growth demonstrating the utility of our analytic approach for decision‐making and operational planning in the fire services.     

Analyzing Hotspots of Crime Using a Bayesian Spatiotemporal Modeling Approach: A Case Study of Violent Crime in the Greater Toronto Area

Conventional methods used to identify crime hotspots at the small‐area scale are frequentist and employ data for one time period. Methodologically, these approaches are limited by an inability to overcome the small number problem, which occurs in spatiotemporal analysis at the small‐area level when crime and population counts for areas are low. The small number problem may lead to unstable risk estimates and unreliable results. Also, conventional approaches use only one data observation per area, providing limited information about the temporal processes influencing hotspots and how law enforcement resources should be allocated to manage crime change. Examining violent crime in the Regional Municipality of York, Ontario, for 2006 and 2007, this research illustrates a Bayesian spatiotemporal modeling approach that analyzes crime trend and identifies hotspots while addressing the small number problem and overcoming limitations of conventional frequentist methods. Specifically, this research tests for an overall trend of violent crime for the study region, determines area‐specific violent crime trends for small‐area units, and identifies hotspots based on crime trend from 2006 to 2007. Overall violent crime trend was found to be insignificant despite increasing area‐specific trends in the north and decreasing area‐specific trends in the southeast. Posterior probabilities of area‐specific trends greater than zero were mapped to identify hotspots, highlighting hotspots in the north of the study region. We discuss the conceptual differences between this Bayesian spatiotemporal method and conventional frequentist approaches as well as the effectiveness of this Bayesian spatiotemporal approach for identifying hotspots from a law enforcement perspective.     

A ROBUST APPROACH TO PREDICTING FLUCTUATIONS IN STATE-LEVEL EMPLOYMENT GROWTH*

ABSTRACT. Economic forecasting models are famous for performing well over short time periods and then suffering rapidly deteriorating performance when economic conditions change. This behavior makes composite forecasting models valuable in situations where large forecast errors cause considerable losses. A composite forecasting model for state-level employment is proposed here. This method is designed to protect state budget processes by producing robust forecasts of changes in employment and the related revenue collections. An application to Georgia nonagricultural employment is presented which demonstrates the benefits of this technique. The example shows that the method can forecast such series accurately without the forecaster having to choose in advance a single model specification to all economic conditions.     

Problems of Long-Term Geographic Prediction

A program of geographic prediction up to the year 2000 is proposed for inclusion in the teaching and research plan of the Geography Faculty of Moscow University. Three aspects are distinguished: (1) specific forecasting of changes on the face of the earth and in the use of resources, (2) the elaboration of methods of geographic prediction, and (3) prediction of future trends in the science of geography. The three key factors in specific geographic prediction are the hydroclimatic, the anthropogenic, and the resource factor. The principal method of prediction should be the study of chain reactions, of cause-and-effect relationships, allowing for flexible multivariate forecasts. In the science of geography, periods of differentiation and integration are found to have alternated at periods of 25 to 30 years. The most promising tendencies in geographic research are the hydroclimatic approach, the new synthetic disciplines, economic geography, and integrated mapping of geographic phenomena.     

THE FUTURE TRAJECTORY OF U.S. CO2 EMISSIONS: THE ROLE OF STATE VS. AGGREGATE INFORMATION*

ABSTRACT This paper provides comparisons of a variety of time‐series methods for short‐run forecasts of the main greenhouse gas, carbon dioxide, for the United States, using a recently released state‐level data set from 1960–2001. We test the out‐of‐sample performance of univariate and multivariate forecasting models by aggregating state‐level forecasts versus forecasting the aggregate directly. We find evidence that forecasting the disaggregate series and accounting for spatial effects drastically improves forecasting performance under root mean squared forecast error loss. Based on the in‐sample observations we attempt to explain the emergence of voluntary efforts by states to reduce greenhouse gas emissions. We find evidence that states with decreasing per capita emissions and a “greener” median voter are more likely to push toward voluntary cutbacks in emissions.     

Leading Indicators and Spatial Interactions: A Crime-Forecasting Model for Proactive Police Deployment

We develop a leading indicator model for forecasting serious property and violent crimes based on the crime attractor and displacement theories of environmental criminology. The model, intended for support of tactical deployment of police resources, is at the microlevel scale; namely, 1-month-ahead forecasts over a grid system of 141 square grid cells 4000 feet on a side (with approximately 100 blocks per grid cell). The leading indicators are selected lesser crimes and incivilities entering the model in two ways: (1) as time lags within grid cells and (2) time and space lags averaged over grid cells contiguous to observation grid cells. Our validation case study uses 1.3 million police records from Pittsburgh, Pennsylvania, aggregated over the grid system for a 96-month period ending in December 1998. The study uses the rolling-horizon forecast experimental design with forecasts made over the 36-month period ending in December 1998, yielding 5076 forecast errors per model. We estimated the leading indicator model using a robust linear regression model, a neural network, and a proven univariate, extrapolative forecast method for use as a benchmark in Granger causality testing. We find evidence of both the crime attractor and displacement theories. The results of comparative forecast experiments are that the leading indicator models provide acceptable forecasts that are significantly better than the extrapolative method in three out of four cases, and for the fourth there is a tie but poor forecast performance. The leading indicators find 41–53% of large crime volume changes in the three successful cases. The corresponding workload for police is quite acceptable, with on the average 5.2 potential large change cases per month to investigate and with 31% of such cases being positives.     

Population geography

The author reviews current trends in the study of population geography and identifies a movement of the subdiscipline during the 1980s toward a possible redefinition as spatial demography. He then outlines the main research topics pursued by population geographers during this period. The geographical scope is worldwide.     

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.     

FORECASTING METROPOLITAN EMPLOYMENT USING AN EXPORT-BASE ERROR-CORBECTION MODEL

This paper shows that a time-series version of export-based location quotients, which decompose area employment into export and local components are co-integrated. Given the statistically established co-integrating relation between the time series for export and local employment, the Granger Representation Theorem tells us that an error-correction mechanism model is the appropriate time-series model specification. A set of forecasting experiments is carried out, in order to test whether the error-correction model produces better long-run forecasts that hold together in economically meaningful ways. Given the computer hardware and software that currently exist, the model developed here represents a simple, low-cost method for accurately forecasting local-area employment in the tradition of past work on export-base models.     

RESEARCH ON THE ARAL SEA AND ARAL REGION

The author, an expert in Central Asian toponymy, culture, and physical geography, examines major trends in research on the Aral region's geography by focusing on major themes of paleogeography, bottom and shoreline morphology, hydroclimatic conditions, migration of salts, and forecasting negative impacts of hydrological engineering projects on the region's environment. In the process he presents much interesting material on the early history of the sea and its hinterland (most notably, the deltas of the Syr Dar'ya and Amu Dar'ya) and about differences in the interpretation of quantitative information about the sea by scholars at different times and using different methodologies.     

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.     

ON THE SEISMIC VULNERABILITY OF EXISTING UNREINFORCED MASONRY BUILDINGS

A large part of the building population in Switzerland is made of unreinforced masonry. For the assessment of the seismic risk the evaluation of the seismic vulnerability of existing unreinforced masonry buildings is therefore crucial. In this paper a method to evaluate existing buildings, which was developed for the earthquake scenario project for Switzerland, is briefly introduced and discussed in more detail for unreinforced masonry buildings. The method is based on a non-linear static approach where the seismic demand on the building is compared with the capacity of the building. In-plane and out-of-plane behaviour are considered. Comparisons with test results from model buildings show that the proposed method suitably forecasts the capacity of a building. Finally, a numerical example of the application of the method to a building in the city of Basel is given.     

THE SPATIOTEMPORAL EVOLUTION OF U.S. CARBON DIOXIDE EMISSIONS: STYLIZED FACTS AND IMPLICATIONS FOR CLIMATE POLICY

We characterize the evolution of U.S. carbon dioxide (CO₂) emissions using an index number decomposition technique which partitions the 1963–2008 growth of states’ energy‐related CO₂ into changes in five driving factors: the emission intensity of energy use, the energy intensity of economic activity, the composition of states’ output, per capita income and population. Compositional change and declining energy intensity attenuate emissions growth, but their impacts are offset by increasing population and income. Despite absolute interstate divergence in both emissions and their precursors, states’ emission‐ and energy intensities—and ultimately, CO₂—appear to be stochastically converging. We assess the implications of these trends using a novel vector autoregression (VAR) emission forecasting technique based on our index numbers. The resulting emission projections are comparable to, but generally exceed, those forecast by the 2010 EIA Annual Energy Outlook.     

Methods of Palaeodemography: The Case of the Iberian Oppida and Roman Cities in North‐East Spain

Ancient demography is a recurrent topic in archaeology, thanks to new methods and evidence from different surveys and excavations. However, different cultures or periods are studied on their own, without any comparison being made between them and of their population dynamics. The present paper seeks to advance the situation by defining methodologies to allow diachronic comparisons between two different periods and cultures. After setting out a methodological approach, the paper goes on to apply the same to a case study: namely the Roman conquest of north‐east Spain, comparing the demography of the ancient Iberian communities (fourth‐second centuries BCE) to the Roman colonization (first century BCE to first century CE). Roman urbanism is generally supposed to increase the population in a particular territory, but our present evidence refutes this point: a decrease in population is visible in urban or proto‐urban sites from the Iberian to Roman periods, though there is an increase in the rural densities.     

Explaining the growth of government expenditure: Australian government department appropriations since world war two

Political economists have concentrated on explaining the growth of total government expenditure rather than individual governmental institutions. This paper applies four models of governmental behaviour from the public administration and public choice areas to the growth of the major Australian government departments in the period 1947–1979, and provides forecasts of expenditure for the years 1980–1982. The three models are: budgetary incrementalism, based on the auto‐correlation of departmental appropriations; the degree of bureaucratic monopoly power vis‐a‐vis its financial sponsor; a macro model incorporating social, political and economic incentives for governmental expenditure (e.g. unemployment); and a model based on measurements of the demand of the median voter for government services. The results suggest that a simple budgetary incrementalism model, using a first order autoregression provides the best explanation for institutional growth over the past thirty years and the best forecasts.     

Forecasting in Economic Geography

Economic-geographic forecasting is intended to predict the dynamics of the interplay between nature and society and to serve as the basis for economic development plans. Because of the large number of variables involved, probability analysis may be expected to play an important role in economic-geographic forecasts. One approach suggested is the analysis of chain reactions involved in various energy-and-production cycles, the name given to integrated economic-geographic systems based on the use of a given set of energy and raw-material sources.