Time series models for beach pollution
Beach pollution was assessed visually by beach inspectors on a five-point ratings scale as 0, 1, 2, 3 or 4 corresponding to pollution levels of None, Low, Trace, Medium or High, respectively. The data consisted of 640 days of pollution ratings at 34 beaches in Sydney, Australia, together with concom...
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| Veröffentlicht in: | Environmental software Jg. 11; H. 1; S. 25 - 33 |
|---|---|
| 1. Verfasser: | |
| Format: | Journal Article Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
1996
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| ISSN: | 0266-9838 |
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| Abstract | Beach pollution was assessed visually by beach inspectors on a five-point ratings scale as 0, 1, 2, 3 or 4 corresponding to pollution levels of None, Low, Trace, Medium or High, respectively. The data consisted of 640 days of pollution ratings at 34 beaches in Sydney, Australia, together with concomitant wind, rain, ocean current and temperature information. Development of statistically significant relationships between the more subjectively measured pollution data and the more objectively quantified physical variables not only served to explain the occurrence of pollution but lent credibility to the ratings scale itself as a useful measure of visual pollution. Methods for analysing qualitative data were combined with time series models to account for the influence of the physical variables, whose effects were subject to delay and dissipation over time. The GLIM statistical language is suitable for modelling ratings data. It is not generally used for time series modelling. Most commonly available time series software is not suitable for ratings data. The application here required a combination of the two methodologies. Special purpose models were formulated and then software written in the GLIM language to estimate time series models with the survey ratings scale data as the dependent variable and the physical data as the independent variables. The work also raises the possibility of developing forecasts for pollution-free days and led to the development of a new estimation scheme for time series models. |
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| AbstractList | Beach pollution was assessed visually by beach inspectors on a five-point ratings scale as 0, 1, 2, 3 or 4 corresponding to pollution levels of None, Low, Trace, Medium or High, respectively. The data consisted of 640 days of pollution ratings at 34 beaches in Sydney, Australia, together with concomitant wind, rain, ocean current and temperature information. Development of statistically significant relationships between the more subjectively measured pollution data and the more objectively quantified physical variables not only served to explain the occurrence of pollution but lent credibility to the ratings scale itself as a useful measure of visual pollution. Methods for analysing qualitative data were combined with time series models to account for the influence of the physical variables, whose effects were subject to delay and dissipation over time. The GLIM statistical language is suitable for modelling ratings data. It is not generally used for time series modelling. Most commonly available time series software is not suitable for ratings data. The application here required a combination of the two methodologies. Special purpose models were formulated and then software written in the GLIM language to estimate time series models with the survey ratings scale data as the dependent variable and the physical data as the independent variables. The work also raises the possibility of developing forecasts for pollution-free days and led to the development of a new estimation scheme for time series models. Beach pollution was assessed visually by beach inspectors on a five-point ratings scale as 0, 1, 2, 3 or 4 corresponding to pollution levels of None, Low, Trace, Medium or High, respectively. The data consisted of 640 days of pollution ratings at 34 beaches in Sydney, Australia, together with concomitant wind, rain, ocean current and temperature information. Development of statistically significant relationships between the more subjectively measured pollution data and the more objectively quantified physical variables not only served to explain the occurrence of pollution but lent credibility to the ratings scale itself as a useful measure of visual pollution. Methods for analyzing qualitative data were combined with time series models to account for the influence of the physical variables, whose effects were subject to delay and dissipation over time. The GLIM statistical language is suitable for modelling ratings data. It is not generally used for time series modelling. Most commonly available time series software is not suitable for ratings data. The application here required a combination of the two methodologies. Special purpose models were formulated and then software written in the GLIM language to estimate time series models with the survey ratings scale data as the dependent variable and the physical data as the independent variables. The work also raises the possibility of developing forecasts for pollution-free days and led to the development of a new estimation scheme for time series models. |
| Author | Jellett, P.M. |
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| Cites_doi | 10.2307/2344614 |
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| Keywords | contingency tables survey data ratings scale pollution Time series |
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| References | Nelder, Wedderburn (BIB2) 1972; A Bishop, Fienberg, Holland (BIB1) 1980 Young (BIB3) 1984 Young (10.1016/S0266-9838(96)00032-9_BIB3) 1984 Nelder (10.1016/S0266-9838(96)00032-9_BIB2) 1972; A Bishop (10.1016/S0266-9838(96)00032-9_BIB1) 1980 |
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| SubjectTerms | Beaches Computer programming languages Computer software contingency tables Data reduction Mathematical models pollution Pollution detection Q1 ratings scale survey data Surveys Time series Time series analysis |
| Title | Time series models for beach pollution |
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