Uploaded byAndrew Clouston
NZ parcel accuracy, visualising the accuracy myth
The document discusses parcel boundaries and the accuracy of the New Zealand cadastre, emphasizing the challenges in obtaining and visualizing boundary accuracy information. It highlights the varying accuracy standards for different types of boundary markers, with an emphasis on the discrepancies between urban and rural areas. The document concludes that cadastral mapping is inherently variable, and inaccuracies do not always indicate errors.
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NZ parcel accuracy, visualising the accuracy myth
- 1. Parcel Boundaries: Visualising theaccuracy myth Andrew Clouston| Geospatial Team Leader Sisi Zhang | Geospatial Analyst ESRI User Conference 2014, Auckland, 19 August 2014
- 2. Overview - The Cadastre -How much is accurate - Obtaining accuracy information - Node accuracy - Visualising Accuracy
- 3. What is theNew Zealand Cadastre?
- 4. Other Crown records tosupport and define boundaries +
- 5.
- 6. Parcel accuracy • 8thOrder = 0.5m accuracy • New Zealand has 5 million boundary nodes not meeting this standard
- 7.
- 8.
- 9.
- 10. 1. Catalogues, weredigitised for easy searching Library Analogy
- 11. 1. Catalogues, weredigitised for easy searching 2. Additional information was added to the catalogue Library Analogy
- 12. 1. Catalogues, weredigitised for easy searching 2. Additional information was added to the catalogue 3. Books were digitised (or created as e-books) and digital catalogue and library merged. Library Analogy
- 13. NZ does notyet have a complete Cadastral Digital Library 1. Catalogues, were digitised for easy searching 2. Additional information was added to the catalogue 3. Books were digitised (or created as e-books) and digital catalogue and library merged. Library Analogy
- 14. How much isok?
- 15. How much isok?
- 16. How much isok?
- 17. How much isok?
- 18.
- 19.
- 20.
- 21. Coordinate Accuracy- LINZG25706 http://www.linz.govt.nz/survey-titles/cadastral-surveying/cadastral-standards/DocumentSummary.aspx%3Fdocument%3D288 Standardfor the geospatial accuracy framework - LINZS25005 http://www.linz.govt.nz/survey-titles/cadastral-surveying/cadastral-standards/DocumentSummary.aspx%3Fdocument%3D255 Standard for tiers, classes, and orders of LINZ data - LINZS25006 http://www.linz.govt.nz/survey-titles/cadastral-surveying/cadastral-standards/DocumentSummary.aspx%3Fdocument%3D256 Getting Accuracy Information
- 22.
- 23. Landonline Accuracies Group OrderOrder purpose Nominal Horizontal Accuracy Geodetic 1 National deformation monitoring 0.05 m 2 Regional Deformation Monitoring 0.1 m 3 0.1 m 4 Local deformation monitoring 0.15 m
- 24. Landonline Accuracies Group OrderOrder purpose Nominal Horizontal Accuracy Geodetic 1 National deformation monitoring 0.05 m 2 Regional Deformation Monitoring 0.1 m 3 0.1 m 4 Local deformation monitoring 0.15 m Survey control 5 Cadastral horizontal control and Basic geospatial network 0.15 m 6 Cadastral permanent reference and witness marks 0.15 m
- 25. Landonline Accuracies Group OrderOrder purpose Nominal Horizontal Accuracy Accurate Boundary Mapping 7 Class A boundary marks (Usually Urban) 0.2 m 8 Class B boundary marks (Usually Peri-Urban) 0.5 m 9 Class C boundary marks (Usually Rural) 5 m
- 26. Landonline Accuracies Group OrderOrder purpose Nominal Horizontal Accuracy Accurate Boundary Mapping 7 Class A boundary marks (Usually Urban) 0.2 m 8 Class B boundary marks (Usually Peri-Urban) 0.5 m 9 Class C boundary marks (Usually Rural) 5 m DCDB based Mapping 10 20 m 11 50 m 12 > 50 m
- 27.
- 28.
- 29. Nodes OK /Not Ok
- 30.
- 32.
- 38.
- 39.
- 40.
- 41.
- 42.
- 43.
- 44.
- 46.
- 47. Boundaries (Linestring) Natural BoundariesParcel Boundaries Vertices=2Vertices>2 Buffer by average accuracy Accuracy Buffer (Polygon)
- 48. Boundaries (Linestring) Natural BoundariesParcel Boundaries Vertices=2Vertices>2 Buffer by average accuracy Accuracy Buffer (Polygon) start_accuracy <> end_accuracy start_accuracy = end_accuracy
- 49. Boundaries (Linestring) Natural BoundariesParcel Boundaries Vertices=2Vertices>2 Buffer by average accuracy Accuracy Buffer (Polygon) Accuracy Buffer (Polygon) Buffer by average accuracy start_accuracy <> end_accuracy start_accuracy = end_accuracy
- 50. Boundaries (Linestring) Natural BoundariesParcel Boundaries Vertices=2Vertices>2 Buffer by average accuracy Accuracy Buffer (Polygon) Accuracy Buffer (Polygon) Buffer by average accuracy start_accuracy <> end_accuracy start_accuracy = end_accuracy Accuracy Buffer (Tropism Polygon) Flag ‘Wrong Direction’ boundaries Buffer by start and end accuracy
- 51. Take home points -Cadastral mapping is variable, especially in rural areas - Inaccuracy is not necessarily an error - Accuracy information is on the LDS - Accuracy can be visualised
- 52.
Editor's Notes
- #4 The cadastre is much more than Cadastral Boundaries. These pictures give a sense of the scope and that it is much wider than just the LINZ records that people access
- #5 The cadastre is much more than Cadastral Boundaries. These pictures give a sense of the scope and that it is much wider than just the LINZ records that people access
- #15 Red is Survey capture areas at the time Landonline rolled out, yellow Spatial Parcel Improvement projects circs 2011. this is essentially 6% of NZ but is more than 70% of parcels and approx 50% of boundary marks.
- #16 Red is Survey capture areas at the time Landonline rolled out, yellow Spatial Parcel Improvement projects circs 2011. this is essentially 6% of NZ but is more than 70% of parcels and approx 50% of boundary marks.
- #17 Left: what it should be like Right : What it actually is (this map is parcel vectors - https://data.linz.govt.nz/layer/820-nz-survey-boundary-vectors/ )
- #18 Left: what it should be like Right : What it actually is (this map is parcel vectors - https://data.linz.govt.nz/layer/820-nz-survey-boundary-vectors/ )
- #19 The extends of cadastral adjustments is another useful layer, There are several, each with their own purposes- refer to the metadata. Search with the word ‘Adjustment’ will locate them
- #20 The extends of cadastral adjustments is another useful layer, There are several, each with their own purposes- refer to the metadata. Search with the word ‘Adjustment’ will locate them
- #22 Quick reference links for those that are interested. There is real benefits if everyone assigned the same accuracy values to their spatial data (along with a date of acquisition) so that data can be easily compared and contrasted.
- #23 These orders are generally not boundary marks, and comprise approximately 0.5% of all nodes in the cadastre
- #24 These orders are generally not boundary marks, and comprise approximately 0.5% of all nodes in the cadastre
- #25 These orders are generally not boundary marks, and comprise approximately 0.5% of all nodes in the cadastre
- #26 The accurate boundary mapping comprises of data capture when Landonline was build and subsequent surveys. The vast majority or order 7 and 8 (approximately 50%) of all nodes lie in survey capture areas. Note that the relative accuracies between these nodes is very good (usually sub decimeter) Order 10-12 nodes can generally trace their lineage back to DCDB mapping, however some new surveys that have been approved and not yet integrated will also be order 10, yet their accuracies will be ‘quite good’
- #27 The accurate boundary mapping comprises of data capture when Landonline was build and subsequent surveys. The vast majority or order 7 and 8 (approximately 50%) of all nodes lie in survey capture areas. Note that the relative accuracies between these nodes is very good (usually sub decimeter) Order 10-12 nodes can generally trace their lineage back to DCDB mapping, however some new surveys that have been approved and not yet integrated will also be order 10, yet their accuracies will be ‘quite good’
- #28 A thematic example of these nodes. Could be considered a bare minimum for user information. This is a poor visualisation as it is relatively unhelpful for most users (too much colour) but is better than nothing
- #29 A thematic example of these nodes. Could be considered a bare minimum for user information. This is a poor visualisation as it is relatively unhelpful for most users (too much colour) but is better than nothing
- #30 A thematic example of these nodes. This is a better representation where the thematic that groups orders 7-9 together for ‘trusted nodes’ and ‘10-12’ for untrusted nodes. Sometime less is more!!
- #31 Comparing the survey areas to the geometric area can identify differences. This map shows the diference (plus or minus) 1-10%. Larger differences usually relate to parcels where the survey area listed may not be correct. This is a crude analysis that highlights distortion
- #32 Comparing the survey areas to the geometric area can identify differences. This map shows the diference (plus or minus) 1-10%. Larger differences usually relate to parcels where the survey area listed may not be correct. This is a crude analysis that highlights distortion
- #33 A better may to visualise the data. Note, The mapping in this example is actually slight worse than the orders would indicate. But it clearly indicates to users the southern boundaries are poorly mapped and should not be relied on
- #34 A better may to visualise the data. Note, The mapping in this example is actually slight worse than the orders would indicate. But it clearly indicates to users the southern boundaries are poorly mapped and should not be relied on
- #35 A better may to visualise the data. Note, The mapping in this example is actually slight worse than the orders would indicate. But it clearly indicates to users the southern boundaries are poorly mapped and should not be relied on
- #36 A better may to visualise the data. Note, The mapping in this example is actually slight worse than the orders would indicate. But it clearly indicates to users the southern boundaries are poorly mapped and should not be relied on
- #37 A better may to visualise the data. Note, The mapping in this example is actually slight worse than the orders would indicate. But it clearly indicates to users the southern boundaries are poorly mapped and should not be relied on
- #38 A better may to visualise the data. Note, The mapping in this example is actually slight worse than the orders would indicate. But it clearly indicates to users the southern boundaries are poorly mapped and should not be relied on
- #39 A better may to visualise the data. Note, The mapping in this example is actually slight worse than the orders would indicate. But it clearly indicates to users the southern boundaries are poorly mapped and should not be relied on
- #40 A better may to visualise the data. Note, The mapping in this example is actually slight worse than the orders would indicate. But it clearly indicates to users the southern boundaries are poorly mapped and should not be relied on
- #41 A better may to visualise the data. Note, The mapping in this example is actually slight worse than the orders would indicate. But it clearly indicates to users the southern boundaries are poorly mapped and should not be relied on
- #42 A better may to visualise the data. Note, The mapping in this example is actually slight worse than the orders would indicate. But it clearly indicates to users the southern boundaries are poorly mapped and should not be relied on
- #43 A better may to visualise the data. Note, The mapping in this example is actually slight worse than the orders would indicate. But it clearly indicates to users the southern boundaries are poorly mapped and should not be relied on
- #45 Diagrammatic methodology for creating the buffers. Note this requires the use of large datasets and the ability to use the full Landonline marks and observations datasets.
- #46 Diagrammatic methodology for creating the buffers. Note this requires the use of large datasets and the ability to use the full Landonline marks and observations datasets.
- #47 Diagrammatic methodology for creating the buffers. Note this requires the use of large datasets and the ability to use the full Landonline marks and observations datasets.
- #48 Diagrammatic methodology for creating the buffers. Note this requires the use of large datasets and the ability to use the full Landonline marks and observations datasets.
- #49 Diagrammatic methodology for creating the buffers. Note this requires the use of large datasets and the ability to use the full Landonline marks and observations datasets.
- #50 Diagrammatic methodology for creating the buffers. Note this requires the use of large datasets and the ability to use the full Landonline marks and observations datasets.
- #51 Diagrammatic methodology for creating the buffers. Note this requires the use of large datasets and the ability to use the full Landonline marks and observations datasets.