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On 28 June 2023 at 8:54:20 am UTC, ckan_admin:
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Added resource AURIN Download Manager to UTAS IRP - Predicted Proportion of Underinsurance (SA1) 2016
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3 | "author_email": "", | 3 | "author_email": "", | ||
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7 | "key": "ADP ID", | 7 | "key": "ADP ID", | ||
8 | "value": | 8 | "value": | ||
9 | "datasource-UTAS_IRP-UoM_AURIN_DB:utas_irp_underinsurance_sa1_2016" | 9 | "datasource-UTAS_IRP-UoM_AURIN_DB:utas_irp_underinsurance_sa1_2016" | ||
10 | }, | 10 | }, | ||
11 | { | 11 | { | ||
12 | "key": "Access Level", | 12 | "key": "Access Level", | ||
13 | "value": "Open Access" | 13 | "value": "Open Access" | ||
14 | }, | 14 | }, | ||
15 | { | 15 | { | ||
16 | "key": "Aggregation Level", | 16 | "key": "Aggregation Level", | ||
17 | "value": "sa1_2016" | 17 | "value": "sa1_2016" | ||
18 | }, | 18 | }, | ||
19 | { | 19 | { | ||
20 | "key": "Attribute List", | 20 | "key": "Attribute List", | ||
21 | "value": "Area (sqkm), GCCSA Code, GCCSA Name, Geometry, | 21 | "value": "Area (sqkm), GCCSA Code, GCCSA Name, Geometry, | ||
22 | Proportion of Underinsurance, SA1 7-Digit Code, SA1 Main Code, SA2 | 22 | Proportion of Underinsurance, SA1 7-Digit Code, SA1 Main Code, SA2 | ||
23 | 5-Digit Code, SA2 Main Code, SA2 Name, SA3 Code, SA3 Name, SA4 Code, | 23 | 5-Digit Code, SA2 Main Code, SA2 Name, SA3 Code, SA3 Name, SA4 Code, | ||
24 | SA4 Name, State Code, State Name" | 24 | SA4 Name, State Code, State Name" | ||
25 | }, | 25 | }, | ||
26 | { | 26 | { | ||
27 | "key": "Attribution", | 27 | "key": "Attribution", | ||
28 | "value": "University of Tasmania - Insurance Research Program, | 28 | "value": "University of Tasmania - Insurance Research Program, | ||
29 | (2019): UTAS IRP - Predicted Proportion of Underinsurance (SA1) 2016; | 29 | (2019): UTAS IRP - Predicted Proportion of Underinsurance (SA1) 2016; | ||
30 | accessed from AURIN on [date of access]." | 30 | accessed from AURIN on [date of access]." | ||
31 | }, | 31 | }, | ||
32 | { | 32 | { | ||
33 | "key": "Coordinate Ref. System", | 33 | "key": "Coordinate Ref. System", | ||
34 | "value": "EPSG:4283 (GDA_1994)" | 34 | "value": "EPSG:4283 (GDA_1994)" | ||
35 | }, | 35 | }, | ||
36 | { | 36 | { | ||
37 | "key": "Copyright Notice", | 37 | "key": "Copyright Notice", | ||
38 | "value": "\u00a9 University of Tasmania 2021" | 38 | "value": "\u00a9 University of Tasmania 2021" | ||
39 | }, | 39 | }, | ||
40 | { | 40 | { | ||
41 | "key": "Data Disclaimer", | 41 | "key": "Data Disclaimer", | ||
42 | "value": "The Under-insurance Mapping Project is the product of | 42 | "value": "The Under-insurance Mapping Project is the product of | ||
43 | analysis of data from the Australian Survey of Social Attitudes 2015 | 43 | analysis of data from the Australian Survey of Social Attitudes 2015 | ||
44 | and the authors acknowledge Australian Consortium for Social and | 44 | and the authors acknowledge Australian Consortium for Social and | ||
45 | Political Research Incorporated as the original depositors of this | 45 | Political Research Incorporated as the original depositors of this | ||
46 | work in the Australian Data Archive. Those who carried out the | 46 | work in the Australian Data Archive. Those who carried out the | ||
47 | original analysis and collection of the data bear no responsibility | 47 | original analysis and collection of the data bear no responsibility | ||
48 | for the further analysis or interpretation of it." | 48 | for the further analysis or interpretation of it." | ||
49 | }, | 49 | }, | ||
50 | { | 50 | { | ||
51 | "key": "Geometry Field", | 51 | "key": "Geometry Field", | ||
52 | "value": "wkb_geometry" | 52 | "value": "wkb_geometry" | ||
53 | }, | 53 | }, | ||
54 | { | 54 | { | ||
55 | "key": "Key", | 55 | "key": "Key", | ||
56 | "value": "sa1_main16" | 56 | "value": "sa1_main16" | ||
57 | }, | 57 | }, | ||
58 | { | 58 | { | ||
59 | "key": "Type", | 59 | "key": "Type", | ||
60 | "value": "dataset" | 60 | "value": "dataset" | ||
61 | }, | 61 | }, | ||
62 | { | 62 | { | ||
63 | "key": "spatial", | 63 | "key": "spatial", | ||
64 | "value": "{\"type\": \"Polygon\", \"coordinates\": [[[96.82, | 64 | "value": "{\"type\": \"Polygon\", \"coordinates\": [[[96.82, | ||
65 | -43.74], [168.0, -43.74], [168.0, -9.14], [96.82, -9.14], [96.82, | 65 | -43.74], [168.0, -43.74], [168.0, -9.14], [96.82, -9.14], [96.82, | ||
66 | -43.74]]]}" | 66 | -43.74]]]}" | ||
67 | } | 67 | } | ||
68 | ], | 68 | ], | ||
69 | "groups": [], | 69 | "groups": [], | ||
70 | "id": "1aa16582-e19c-4e51-a6d9-e0904472eaa3", | 70 | "id": "1aa16582-e19c-4e51-a6d9-e0904472eaa3", | ||
71 | "isopen": true, | 71 | "isopen": true, | ||
72 | "license_id": "CC-BY-NC-4.0", | 72 | "license_id": "CC-BY-NC-4.0", | ||
73 | "license_title": "Creative Commons Attribution-NonCommercial 4.0 | 73 | "license_title": "Creative Commons Attribution-NonCommercial 4.0 | ||
74 | International (CC BY-NC 4.0)", | 74 | International (CC BY-NC 4.0)", | ||
75 | "license_url": "https://creativecommons.org/licenses/by-nc/4.0/", | 75 | "license_url": "https://creativecommons.org/licenses/by-nc/4.0/", | ||
76 | "maintainer": null, | 76 | "maintainer": null, | ||
77 | "maintainer_email": null, | 77 | "maintainer_email": null, | ||
78 | "metadata_created": "2023-06-28T08:54:19.997714", | 78 | "metadata_created": "2023-06-28T08:54:19.997714", | ||
n | 79 | "metadata_modified": "2023-06-28T08:54:19.997729", | n | 79 | "metadata_modified": "2023-06-28T08:54:20.521783", |
80 | "name": "utas-irp-utas-irp-underinsurance-sa1-2016-sa1-2016", | 80 | "name": "utas-irp-utas-irp-underinsurance-sa1-2016-sa1-2016", | ||
81 | "notes": "This dataset presents the footprint of the proportion of | 81 | "notes": "This dataset presents the footprint of the proportion of | ||
82 | underinsurance across Australia. The data is aggregated to Statistical | 82 | underinsurance across Australia. The data is aggregated to Statistical | ||
83 | Area Level 1 (SA1) geographic areas from the 2016 Australian | 83 | Area Level 1 (SA1) geographic areas from the 2016 Australian | ||
84 | Statistical Geography Standard (ASGS). House and contents | 84 | Statistical Geography Standard (ASGS). House and contents | ||
85 | underinsurance is understood as homeowners having no house insurance | 85 | underinsurance is understood as homeowners having no house insurance | ||
86 | and renters having no contents insurance to cover adverse events. | 86 | and renters having no contents insurance to cover adverse events. | ||
87 | \n\nTo create this dataset, researchers developed a method to | 87 | \n\nTo create this dataset, researchers developed a method to | ||
88 | extrapolate the patterns of underinsurance evident in the [2015 | 88 | extrapolate the patterns of underinsurance evident in the [2015 | ||
89 | Australian Survey of Social Attitudes | 89 | Australian Survey of Social Attitudes | ||
90 | ataverse.ada.edu.au/dataset.xhtml?persistentId=doi:10.4225/87/T5BNZ7), | 90 | ataverse.ada.edu.au/dataset.xhtml?persistentId=doi:10.4225/87/T5BNZ7), | ||
91 | an omnibus postal survey of Australian adults (Blunsdon, 2016). To do | 91 | an omnibus postal survey of Australian adults (Blunsdon, 2016). To do | ||
92 | this, they combined the results of the full model of underinsurance | 92 | this, they combined the results of the full model of underinsurance | ||
93 | with the [2016 Socio-Economic Indexes for Areas | 93 | with the [2016 Socio-Economic Indexes for Areas | ||
94 | (SEIFA)](https://www.abs.gov.au/ausstats/abs@.nsf/mf/2033.0.55.001) | 94 | (SEIFA)](https://www.abs.gov.au/ausstats/abs@.nsf/mf/2033.0.55.001) | ||
95 | (Australian Bureau of Statistics, 2019). For this spatial mapping, | 95 | (Australian Bureau of Statistics, 2019). For this spatial mapping, | ||
96 | regression coefficients were converted to probabilities by taking the | 96 | regression coefficients were converted to probabilities by taking the | ||
97 | exponent of each coefficient to generate the odds ratio and then using | 97 | exponent of each coefficient to generate the odds ratio and then using | ||
98 | the formula: probability = odds/(1+odds). For each SA1 unit | 98 | the formula: probability = odds/(1+odds). For each SA1 unit | ||
99 | (containing approximately 150 households), the proportion of residents | 99 | (containing approximately 150 households), the proportion of residents | ||
100 | or households was determined for each predictor variable from raw | 100 | or households was determined for each predictor variable from raw | ||
101 | census data. The level of underinsurance (proportion of people | 101 | census data. The level of underinsurance (proportion of people | ||
102 | predicted not to have insurance) was then predicted separately for | 102 | predicted not to have insurance) was then predicted separately for | ||
103 | renters and owner-occupiers for every SA1 and a single map generated | 103 | renters and owner-occupiers for every SA1 and a single map generated | ||
104 | by weighting the predictions by the proportion of renters and | 104 | by weighting the predictions by the proportion of renters and | ||
105 | owner-occupiers per SA1.\n\nFor further information about this dataset | 105 | owner-occupiers per SA1.\n\nFor further information about this dataset | ||
106 | and its creation, please refer to the publication: [Booth, K., & | 106 | and its creation, please refer to the publication: [Booth, K., & | ||
107 | Kendal, D. (2019). Underinsurance as adaptation: Household agency in | 107 | Kendal, D. (2019). Underinsurance as adaptation: Household agency in | ||
108 | places of marketisation and financialisation. Environment and Planning | 108 | places of marketisation and financialisation. Environment and Planning | ||
109 | A: Economy and | 109 | A: Economy and | ||
110 | Space](https://doi.org/10.1177/0308518X19879165).\n\nPlease note:\n\n | 110 | Space](https://doi.org/10.1177/0308518X19879165).\n\nPlease note:\n\n | ||
111 | * The researchers acknowledge some limitations with the data, | 111 | * The researchers acknowledge some limitations with the data, | ||
112 | including the lack of data on rental properties. They do not know | 112 | including the lack of data on rental properties. They do not know | ||
113 | whether these properties are insured by landlord-investors and how | 113 | whether these properties are insured by landlord-investors and how | ||
114 | this may be associated with sociodemographic variables and contribute | 114 | this may be associated with sociodemographic variables and contribute | ||
115 | to the mapping.\n\n * This research was in part supported by the | 115 | to the mapping.\n\n * This research was in part supported by the | ||
116 | Australian Government through the Australian Research Council | 116 | Australian Government through the Australian Research Council | ||
117 | Discovery Program (DP170100096).\n", | 117 | Discovery Program (DP170100096).\n", | ||
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120 | "organization": { | 120 | "organization": { | ||
121 | "approval_status": "approved", | 121 | "approval_status": "approved", | ||
122 | "created": "2023-06-28T06:14:50.600428", | 122 | "created": "2023-06-28T06:14:50.600428", | ||
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160 | ], | 184 | ], | ||
161 | "title": "UTAS IRP - Predicted Proportion of Underinsurance (SA1) | 185 | "title": "UTAS IRP - Predicted Proportion of Underinsurance (SA1) | ||
162 | 2016", | 186 | 2016", | ||
163 | "type": "dataset", | 187 | "type": "dataset", | ||
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