An adaptive filter for estimating spatially-varying parameters: application to modeling police hours spent in response to calls for service

Article Abstract:

A Spatial Adaptive Filter (SAF) is presented that uses generalized damped negative feedback for estimating spatially-varying parameters for multivariate models. Earlier adaptive filters were intended for estimating time-varying parameters and processing data recursively in time sequence, but SAF processes all data at one time in an iterative algorithm. SAF is shown by Monte Carlo studies to successfully identify and estimate step-jump and continuous spatial variation automatically in the parameters of causal variables. Police vehicle hours spent answering calls is related to socio-economic indicators as a case study. It is shown that this study has systematic spatial variation in estimated parameters, and that independent variables significant in inner-city areas grow increasingly less significant in outer-city areas.

Usage, Forecasts and trends, Comparative analysis, Mathematical models, Multivariate analysis, Police administration, Police departments, Citizen crime reporting

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An adaptive filtering procedure for estimating regression quantiles

Article Abstract:

A quantile estimation procedure (QEP) is developed to trigger preventive actions to eliminate undesirable states in systems with applications of reliability theory and chance-constrained programming. The QEP developed constitutes an adaptive filter with nonparametric estimates of parameters that vary over time, considering multivariate regression quantiles. Monte Carlo analysis of the QEP verifies its application to a number of stochastic and random occurrences, such as the monitoring of compliance with short-term air quality standards.

Research, Time-series analysis, Time series analysis

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Emergency government interventions: case study of natural gas shortages

Article Abstract:

Three decision models and methods are presented for improving intervention by governments, and a case study is also given of a natural gas shortage in Ohio during 1976 to 1977. The decision models presented proceed progressively from regulated allocations to directives not based on price. Results are given on chance constraints that enable time series forecasts for decision making. According to the models, some of the decisions taken during the Ohio natural gas shortage were probably unnecessary.

United States, Management, Case studies, Economic aspects, Supply and demand, Natural gas, Gas industry, Ohio, Disaster relief, Fuel oil, Fuel oils

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