Context

With the arrival of the COVID19 virus in New Zealand, the ministry of health is tracking new cases and releasing daily updates on the situation on their webpage: https://www.health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-current-cases and https://www.health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-current-cases/covid-19-current-cases-details. Much of the information given in these updates are not in a machine-friendly format. The objective of this dataset is to provide NZ Minstry of Health COVID19 data in easy-to-use format.

Content

All data in this dataset has been acquired from the New Zealand Minstry of Health's 'COVID19 current cases' webpage, located here: https://www.health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-current-cases. The Ministry of Health updates their page daily, that will be the targeted update frequency for this dataset for the Daily Count of Cases dataset. The Case Details dataset which includes travel details on each case will be updated weekly.

Limitations of this dataset

The mission of this project is to reliably convey data that the Ministry of Health has reported in the most digestable format. Enrichment of data is currently out of scope.

Help improve this dataset

If you find any discrepancies between the Ministry of Health's data and this dataset, please provide your feedback as an issue on the git repo for this dataset: https://github.com/2kruman/COVID19-NZ-known-cases/issues.

New Zealand recorded 2282861 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, New Zealand reported 2792 Coronavirus Deaths. This dataset includes a chart with historical data for New Zealand Coronavirus Cases.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datasets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of acquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis: - Analyze missing data: Project Tycho datasets do not include time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. - Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

Context

Looking for something other than US Police data? Me too. Uploaded some of my local data, in case others wanted to play with it too. Thought Kaggle should have something from NZ.

Content

This is NZ Police crime data. More specifically, it's victimisation data focused on time and place - crimes which had a recorded victim. It gets broken down by region and Police district, and types of crimes. It covers from July 2014 to March 2021

Acknowledgements

Thanks to NZ Police for providing this in an accessible and easy to export from tool - a tableau dashboard on their public website.

Additional CC4.0BY details: Crown copyright ©. Copyright material linked from the policedata.nz webpage is protected by copyright owned by New Zealand Police on behalf of the Crown. Unless indicated otherwise for specific items or collections of content, this copyright material is licensed for re-use under a Creative Commons Attribution 4.0 International Licence. In essence, you are free to copy, distribute and adapt the material, as long as you attribute it to New Zealand Police and abide by the other licence terms.

Inspiration

What can you visualise with this? How can you make the most of all the categorical variables? Anyone else want some dirty data to practice going end to end with?

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datasets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of acquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis: - Analyze missing data: Project Tycho datasets do not include time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. - Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

This Australian and New Zealand food category cost dataset was created to inform diet and economic modelling for low and medium socioeconomic households in Australia and New Zealand. The dataset was created according to the INFORMAS protocol, which details the methods to systematically and consistently collect and analyse information on the price of foods, meals and affordability of diets in different countries globally. Food categories were informed by the Food Standards Australian New Zealand (FSANZ) AUSNUT (AUStralian Food and NUTrient Database) 2011-13 database, with additional food categories created to account for frequently consumed and culturally important foods.

Methods The dataset was created according to the INFORMAS protocol [1], which detailed the methods to collect and analyse information systematically and consistently on the price of foods, meals, and affordability of diets in different countries globally.

Cost data were collected from four supermarkets in each country: Australia and New Zealand. In Australia, two (Coles Merrylands and Woolworths Auburn) were located in a low and two (Coles Zetland and Woolworths Burwood) were located in a medium metropolitan socioeconomic area in New South Wales from 7-11th December 2020. In New Zealand, two (Countdown Hamilton Central and Pak ‘n Save Hamilton Lake) were located in a low and two (Countdown Rototuna North and Pak ‘n Save Rosa Birch Park) in a medium socioeconomic area in the North Island, from 16-18th December 2020.

Locations in Australia were selected based on the Australian Bureau of Statistics Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD) [2]. The index ranks areas from most disadvantaged to most advantaged using a scale of 1 to 10. IRSAD quintile 1 was chosen to represent low socio-economic status and quintile 3 for medium SES socio-economic status. Locations in New Zealand were chosen using the 2018 NZ Index of Deprivation and statistical area 2 boundaries [3]. Low socio-economic areas were defined by deciles 8-10 and medium socio-economic areas by deciles 4-6. The supermarket locations were chosen according to accessibility to researchers. Data were collected by five trained researchers with qualifications in nutrition and dietetics and/or nutrition science.

All foods were aggregated into a reduced number of food categories informed by the Food Standards Australian New Zealand (FSANZ) AUSNUT (AUStralian Food and NUTrient Database) 2011-13 database, with additional food categories created to account for frequently consumed and culturally important foods. Nutrient data for each food category can therefore be linked to the Australian Food and Nutrient (AUSNUT) 2011-13 database [4] and NZ Food Composition Database (NZFCDB) [5] using the 8-digit codes provided for Australia and New Zealand, respectively.

Data were collected for three representative foods within each food category, based on criteria used in the INFORMAS protocol: (i) the lowest non-discounted price was chosen from the most commonly available product size, (ii) the produce was available nationally, (iii) fresh produce of poor quality was omitted. One sample was collected per representative food product per store, leading to a total of 12 food price samples for each food category. The exception was for the ‘breakfast cereal, unfortified, sugars ≤15g/100g’ food category in the NZ dataset, which included only four food price samples because only one representative product per supermarket was identified.

Variables in this dataset include: (i) food category and description, (ii) brand and name of representative food, (iii) product size, (iv) cost per product, and (v) 8-digit code to link product to nutrient composition data (AUSNUT and NZFCDB).

References

Vandevijvere, S.; Mackay, S.; Waterlander, W. INFORMAS Protocol: Food Prices Module [Internet]. Available online: https://auckland.figshare.com/articles/journal_contribution/INFORMAS_Protocol_Food_Prices_Module/5627440/1 (accessed on 25 October).
2071.0 - Census of Population and Housing: Reflecting Australia - Stories from the Census, 2016 Available online: https://www.abs.gov.au/ausstats/abs@.nsf/Lookup/by Subject/2071.0~2016~Main Features~Socio-Economic Advantage and Disadvantage~123 (accessed on 10 December).
Socioeconomic Deprivation Indexes: NZDep and NZiDep, Department of Public Health. Available online: https://www.otago.ac.nz/wellington/departments/publichealth/research/hirp/otago020194.html#2018 (accessed on 10 December)
AUSNUT 2011-2013 food nutrient database. Available online: https://www.foodstandards.gov.au/science/monitoringnutrients/ausnut/ausnutdatafiles/Pages/foodnutrient.aspx (accessed on 15 November).
NZ Food Composition Data. Available online: https://www.foodcomposition.co.nz/ (accessed on 10 December)

Usage Notes The uploaded data includes an Excel spreadsheet where a separate worksheet is provided for the Australian food price database and New Zealand food price database, respectively. All cost data are presented to two decimal points, and the mean and standard deviation of each food category is presented. For some representative foods in NZ, the only NFCDB food code available was for a cooked product, whereas the product is purchased raw and cooked prior to eating, undergoing a change in weight between the raw and cooked versions. In these cases, a conversion factor was used to account for the weight difference between the raw and cooked versions, to ensure that nutrient information (on accessing from the NZFCDB) was accurate. This conversion factor was developed based on the weight differences between the cooked and raw versions, and checked for accuracy by comparing quantities of key nutrients in the cooked vs raw versions of the product.

This New Zealand Point Cloud Classification Deep Learning Package will classify point clouds into tree and background classes. This model is optimized to work with New Zealand aerial LiDAR data.The classification of point cloud datasets to identify Trees is useful in applications such as high-quality 3D basemap creation, urban planning, forestry workflows, and planning climate change response.Trees could have a complex irregular geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.This model is designed to extract Tree in both urban and rural area in New Zealand.The Training/Testing/Validation dataset are taken within New Zealand resulting of a high reliability to recognize the pattern of NZ common building architecture.Licensing requirementsArcGIS Desktop - ArcGIS 3D Analyst extension for ArcGIS ProUsing the modelThe model can be used in ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.InputThe model is trained with classified LiDAR that follows the LINZ base specification. The input data should be similar to this specification.Note: The model is dependent on additional attributes such as Intensity, Number of Returns, etc, similar to the LINZ base specification. This model is trained to work on classified and unclassified point clouds that are in a projected coordinate system, in which the units of X, Y and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly. The model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification and scenarios with false positives.The model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. Another example where fine-tuning this model can be useful is when the object of interest is tram wires, railway wires, etc. which are geometrically similar to electricity wires. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 5 Trees / High-vegetationApplicable geographiesThe model is expected to work well in the New Zealand. It's seen to produce favorable results as shown in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Training dataset - Wellington CityTesting dataset - Tawa CityValidation/Evaluation dataset - Christchurch City Dataset City Training Wellington Testing Tawa Validating ChristchurchModel architectureThis model uses the PointCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Never Classified 0.991200 0.975404 0.983239 High Vegetation 0.933569 0.975559 0.954102Training dataThis model is trained on classified dataset originally provided by Open TopoGraphy with < 1% of manual labelling and correction.Train-Test split percentage {Train: 80%, Test: 20%} Chosen this ratio based on the analysis from previous epoch statistics which appears to have a descent improvementThe training data used has the following characteristics: X, Y, and Z linear unitMeter Z range-121.69 m to 26.84 m Number of Returns1 to 5 Intensity16 to 65520 Point spacing0.2 ± 0.1 Scan angle-15 to +15 Maximum points per block8192 Block Size20 Meters Class structure[0, 5]Sample resultsModel to classify a dataset with 5pts/m density Christchurch city dataset. The model's performance are directly proportional to the dataset point density and noise exlcuded point clouds.To learn how to use this model, see this story

Context

This dataset was collected from data received via this APi.

Content

“[Recovered cases are a] more important metric to track than Confirmed cases.”— Researchers for the University of Virginia’s COVID-19 dashboard

If the number of total cases were accurately known for every country then the number of cases per million people would be a good indicator as to how well various countries are handling the pandemic.

Countries:

Datasets contend data about covid_19 from 232 countries - Afghanistan - Albania - Algeria - Andorra - Angola - Anguilla - Antigua-and-Barbuda - Argentina - Armenia - Aruba - Australia - Austria - Azerbaijan - Bahamas - Bahrain - Bangladesh - Barbados - Belarus - Belgium - Belize - Benin - Bermuda - Bhutan - Bolivia - Bosnia-and-Herzegovina - Botswana - Brazil - British-Virgin-Islands - Brunei - Bulgaria - Burkina-Faso - Burundi - Cabo-Verde - Cambodia - Cameroon - Canada - CAR - Caribbean-Netherlands - Cayman-Islands - Chad - Channel-Islands - Chile - China - Colombia - Comoros - Congo - Cook-Islands - Costa-Rica - Croatia - Cuba - Curaçao - Cyprus - Czechia - Denmark - Diamond-Princess - Diamond-Princess- - Djibouti - Dominica - Dominican-Republic - DRC - Ecuador - Egypt - El-Salvador - Equatorial-Guinea - Eritrea - Estonia - Eswatini - Ethiopia - Faeroe-Islands - Falkland-Islands - Fiji - Finland - France - French-Guiana - French-Polynesia - Gabon - Gambia - Georgia - Germany - Ghana - Gibraltar - Greece - Greenland - Grenada - Guadeloupe - Guam - Guatemala - Guinea - Guinea-Bissau - Guyana - Haiti - Honduras - Hong-Kong - Hungary - Iceland - India - Indonesia - Iran - Iraq - Ireland - Isle-of-Man - Israel - Italy - Ivory-Coast - Jamaica - Japan - Jordan - Kazakhstan - Kenya - Kiribati - Kuwait - Kyrgyzstan - Laos - Latvia - Lebanon - Lesotho - Liberia - Libya - Liechtenstein - Lithuania - Luxembourg - Macao - Madagascar - Malawi - Malaysia - Maldives - Mali - Malta - Marshall-Islands - Martinique - Mauritania - Mauritius - Mayotte - Mexico - Micronesia - Moldova - Monaco - Mongolia - Montenegro - Montserrat - Morocco - Mozambique - MS-Zaandam - MS-Zaandam- - Myanmar - Namibia - Nepal - Netherlands - New-Caledonia - New-Zealand - Nicaragua - Niger - Nigeria - Niue - North-Macedonia - Norway - Oman - Pakistan - Palau - Palestine - Panama - Papua-New-Guinea - Paraguay - Peru - Philippines - Poland - Portugal - Puerto-Rico - Qatar - Réunion - Romania - Russia - Rwanda - S-Korea - Saint-Helena - Saint-Kitts-and-Nevis - Saint-Lucia - Saint-Martin - Saint-Pierre-Miquelon - Samoa - San-Marino - Sao-Tome-and-Principe - Saudi-Arabia - Senegal - Serbia - Seychelles - Sierra-Leone - Singapore - Sint-Maarten - Slovakia - Slovenia - Solomon-Islands - Somalia - South-Africa - South-Sudan - Spain - Sri-Lanka - St-Barth - St-Vincent-Grenadines - Sudan - Suriname - Sweden - Switzerland - Syria - Taiwan - Tajikistan - Tanzania - Thailand - Timor-Leste - Togo - Tonga - Trinidad-and-Tobago - Tunisia - Turkey - Turks-and-Caicos - UAE - Uganda - UK - Ukraine - Uruguay - US-Virgin-Islands - USA - Uzbekistan - Vanuatu - Vatican-City - Venezuela - Vietnam - Wallis-and-Futuna - Western-Sahara - Yemen - Zambia - Zimbabw-

ps-places-metadata-v1.01

SUMMARY

This dataset comprises a pair of layers, (points and polys) which attempt to better locate "populated places" in NZ. Populated places are defined here as settled areas, either urban or rural where densitys of around 20 persons per hectare exist, and something is able to be seen from the air.

RATIONALE

The only liberally licensed placename dataset is currently LINZ geographic placenames, which has the following drawbacks: - coordinates are not place centers but left most label on 260 series map - the attributes are outdated

METHODOLOGY

This dataset necessarily involves cleaving the linz placenames set into two, those places that are poplulated, and those unpopulated. Work was carried out in four steps. First placenames were shortlisted according to the following criterion: - all places that rated at least POPL in the linz geographic places layer, ie POPL, METR or TOWN or USAT were adopted. - Then many additional points were added from a statnz meshblock density analysis.
- Finally remaining points were added from a check against linz residential polys, and zenbu poi clusters.

Spelling is broadly as per linz placenames, but there are differences for no particular reason. Instances of LINZ all upper case have been converted to sentance case. Some places not presently in the linz dataset are included in this set, usually new places, or those otherwise unnamed. They appear with no linz id, and are not authoritative, in some cases just wild guesses.

Density was derived from the 06 meshblock boundarys (level 2, geometry fixed), multipart conversion, merging in 06 usually resident MB population then using the formula pop/area*10000. An initial urban/rural threshold level of 0.6 persons per hectare was used.

Step two was to trace the approx extent of each populated place. The main purpose of this step was to determine the relative area of each place, and to create an intersection with meshblocks for population. Step 3 involved determining the political center of each place, broadly defined as the commercial center.

Tracing was carried out at 1:9000 for small places, and 1:18000 for large places using either bing or google satellite views. No attempt was made to relate to actual town 'boundarys'. For example large parks or raceways on the urban fringe were not generally included. Outlying industrial areas were included somewhat erratically depending on their connection to urban areas.

Step 3 involved determining the centers of each place. Points were overlaid over the following layers by way of a base reference:

a. original linz placenames b. OSM nz-locations points layer c. zenbu pois, latest set as of 5/4/11 d. zenbu AllSuburbsRegions dataset (a heavily hand modified) LINZ BDE extract derived dataset courtesy Zenbu. e. LINZ road-centerlines, sealed and highway f. LINZ residential areas, g. LINZ building-locations and building footprints h. Olivier and Co nz-urban-north and south

Therefore in practice, sources c and e, form the effective basis of the point coordinates in this dataset. Be aware that e, f and g are referenced to the LINZ topo data, while c and d are likely referenced to whatever roading dataset google possesses. As such minor discrepencys may occur when moving from one to the other.

Regardless of the above, this place centers dataset was created using the following criteria, in order of priority:

• attempts to represent the present (2011) subjective 'center' of each place as defined by its commercial/retail center ie. mainstreets where they exist, any kind of central retail cluster, even a single shop in very small places.
• the coordinate is almost always at the junction of two or more roads.
• most of the time the coordinate is at or near the centroid of the poi cluster
• failing any significant retail presence, the coordinate tends to be placed near the main road junction to the community.
• when the above criteria fail to yield a definitive answer, the final criteria involves the centroids of: . the urban polygons . the clusters of building footprints/locations.

To be clear the coordinates are manually produced by eye without any kind of computation. As such the points are placed approximately perhaps plus or minus 10m, but given that the roads layers are not that flash, no attempt was made to actually snap the coordinates to the road junctions themselves.

The final step involved merging in population from SNZ meshblocks (merge+sum by location) of popl polys). Be aware that due to the inconsistent way that meshblocks are defined this will result in inaccurate populations, particular small places will collect population from their surrounding area. In any case the population will generally always overestimate by including meshblocks that just nicked the place poly. Also there are a couple of dozen cases of overlapping meshblocks between two place polys and these will double count. Which i have so far made no attempt to fix.

Merged in also tla and regions from SNZ shapes, a few of the original linz atrributes, and lastly grading the size of urban areas according to SNZ 'urban areas" criteria. Ie: class codes:

1. Not used.
2. main urban area 30K+
3. secondary urban area 10k-30K
4. minor urban area 1k-10k
5. rural center 300-1K
6. village -300

Note that while this terminology is shared with SNZ the actual places differ owing to different decisions being made about where one area ends an another starts, and what constiutes a suburb or satellite. I expect some discussion around this issue. For example i have included tinwald and washdyke as part of ashburton and timaru, but not richmond or waikawa as part of nelson and picton. Im open to discussion on these.

No attempt has or will likely ever be made to locate the entire LOC and SBRB data subsets. We will just have to wait for NZFS to release what is thought to be an authoritative set.

PROJECTION

Shapefiles are all nztm. Orig data from SNZ and LINZ was all sourced in nztm, via koordinates, or SNZ. Satellite tracings were in spherical mercator/wgs84 and converted to nztm by Qgis. Zenbu POIS were also similarly converted.

ATTRIBUTES

Shapefile: Points id : integer unique to dataset name : name of popl place, string class : urban area size as above. integer tcode : SNZ tla code, integer rcode : SNZ region code, 1-16, integer area : area of poly place features, integer in square meters. pop : 2006 usually resident popluation, being the sum of meshblocks that intersect the place poly features. Integer lid : linz geog places id desc_code : linz geog places place type code

Shapefile: Polygons gid : integer unique to dataset, shared by points and polys name : name of popl place, string, where spelling conflicts occur points wins area : place poly area, m2 Integer

LICENSE

Clarification about the minorly derived nature of LINZ and google data needs to be sought. But pending these copyright complications, the actual points data is essentially an original work, released as public domain. I retain no copyright, nor any responsibility for data accuracy, either as is, or regardless of any changes that are subsequently made to it.

Peter Scott 16/6/2011

v1.01 minor spelling and grammar edits 17/6/11

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team, except for aggregation of individual case count data into daily counts when that was the best data available for a disease and location. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format. All geographic locations at the country and admin1 level have been represented at the same geographic level as in the data source, provided an ISO code or codes could be identified, unless the data source specifies that the location is listed at an inaccurate geographical level. For more information about decisions made by the curation team, recommended data processing steps, and the data sources used, please see the README that is included in the dataset download ZIP file.

The National Transfusion Dataset (NTD) is a collection of transfusion episode data incorporating transfusion, laboratory and hospital data from hospitals and health services, as well as prehospital transfusion data from ambulance and retrieval services.

The NTD will form the first integrated national database of blood usage in Australia. The NTD aims to collect information about where, when, and how blood products are used across all clinical settings. This will address Australia’s absence of an integrated national database to record blood usage with the ability to link with clinical outcomes. The dataset will be an invaluable resource towards a comprehensive understanding of how and why blood products are used, numbers and characteristics of patients transfused in health services, the clinical outcomes after transfusion; and provide support to policy development and research.

The NTD was formed through the incorporation of the established Australian and New Zealand Massive Transfusion Registry (ANZ-MTR) and a pilot Transfusion Database (TD) project. The ANZ-MTR has a unique focus on massive transfusion (MT) and contains over 10,000 cases from 41 hospitals across Australia and New Zealand. The TD was a trial extension of the registry that collated data on ALL (not just massive) transfusions on >8000 patients from pilot hospitals. The NTD will integrate and expand these databases to provide new data on transfusion practice including blood utilisation, clinical management and the vital closing of the haemovigilance loop.

Conditions of use:

Any material or manuscript to be published using NTD data must be submitted for review by the NTD Steering Committee prior to submission for publication. The NTD, and Partner Organisations should be acknowledged in all publications. Preferred wording for the acknowledgement will be provided with the data. The NTD reserves the right to dissociate itself from conclusions drawn if it deems necessary.

If the data is the primary source for a report or publication, the source of the data must be acknowledged, along with a statement that the analysis and interpretation are those of the author, not the NTD. Where an author analysing the data is a member of an organisation formally associated, or partnered with the NTD, the NTD should be acknowledged as a secondary affiliation. Where the author is a member of the NTD Project Team, then the primary attribution should be the NTD. The dataset DOI (10.26180/22151987) must be referenced in all publications.

Further information can be found in the Data Access and Publications Policy.

To submit a data access request click here.

Authors: L.G. Garrett1, M.S. Watt2, C.W. Ross3, G. Coker2, M.R. Davis2, J. Sanderman4, R. Parfitt3, J. Dando3, R. Simcock5, D.J. Palmer2, F. Dean1, S. Patel1, J.H. Bridson1, T. Carlin2, T. Payn1, B. Richardson1, A. Dunningham1, P.W. Clinton2. Affiliation: 1 Scion, Private Bag 3020, Rotorua 3046, New Zealand; 2 Scion, PO Box 29237, Riccarton, Christchurch 8440, New Zealand; 3 Manaaki Whenua – Landcare Research, Private Bag 11052, Palmerston North, New Zealand; 4 Woodwell Climate Research Center, 149 Woods Hole Road, Falmouth, MA 02540, USA; 5 Manaaki Whenua – Landcare Research, Private Bag 92170, Auckland, New Zealand. A soil dataset from the FR380 trial series spanning 35 Pinus radiata forest sites in New Zealand. The dataset underpins three existing publications by Watt et al. (2005; 2008) and Ross et al. (2009) which details sample sites and the time zero (i.e. time of tree planting) sample collection and testing method. The publication by Garrett et al., (2022) details the soil mid-infrared spectroscopy method and extension on soil chemistry testing using the same time zero samples.
The data is identified by an individual trial site ID and soil profile ID. Individual samples collected from site/soil profile are then identified by an individual soil horizon number and lab letter. Soil chemistry testing was undertaken at two laboratories, Manaaki Whenua - Landcare Research and Scion, which allocated individual lab ID’s. The linkage between these two sample ID’s, allocated to the same sample, are shown in the file ‘FR380_chemical’. The MIR spectra files use the Scion lab sample ID.
The data includes: · File ‘FR380_sitedescription’: FR380 trial site description by trial ID and soil profile ID, including site location and description, soil classification, land use at time of trial installation and forest rotation number. · File ‘’FR380_soilprofile’: FR380 trial site soil profile description by trial ID, soil profile ID and horizon number. · File ‘FR380_chemical’: FR380 trial soil chemical properties by trial ID, soil profile ID, horizon number and lab letter, and induvial laboratory soil chemistry sample ID from both Manaaki Whenua - Landcare Research and Scion. · File ‘FR380_particlesize: FR380 trial soil particle size properties by trial ID, soil profile ID and horizon number and lab letter. · File ‘FR380_physical’: FR380 trial soil physical properties by trial ID, soil profile ID, horizon number and lab letter. · Folder ‘FR380_MIR spectra’: FR380 trial soil Mid-Infrared spectra opus files by Scion sample ID. · Folder ‘FR380_MIR spectra_csv’: FR380 trial soil Mid-Infrared spectra csv files by Scion sample ID.
· Folder ‘FR380_soil profile images’: FR380 trial soil profile image files by trial site ID.
Contact: Loretta Garrett (loretta.garrett@scionresearch.com) Acknowledgments Funding to publish the data came from the Tree-Root-Microbiome programme, which is funded by Ministry of Business, Innovation & Employment (MBIE) Endeavour Fund and in part by the New Zealand Forest Growers Levy Trust (C04X2002). Funding for the soil spectroscopy data and extension of soil chemical properties came from the Resilient Forest programme, which is funded by New Zealand Ministry of Business, Innovation & Employment (MBIE) Strategic Science Investment Fund, and in part by the New Zealand Forest Growers Levy Trust (C04X1703) and the Tree-Root-Microbiome programme (C04X2002). Funding for the sample collection and initial testing was provided from the Protecting and Enhancing the Environment through Forestry, which was funded by the New Zealand Foundation for Research, Science and Technology (C04X0304). Sites for the trial series were provided by numerous forest companies and private land owners, for which we are grateful. Individual laboratories who provided soil analyses are identified in the dataset and thanked. References Garrett LG, Sanderman J, Palmer DJ, Dean F, Patel S, Bridson JH, Carlin T (2022) Mid-infrared spectroscopy for planted forest soil and foliage nutrition predictions, New Zealand case study. Trees, Forests and People 8: 100280. https://doi.org/10.1016/j.tfp.2022.100280 Ross, C.W., Watt, M.S., Parfitt, R.L., Simcock, R., Dando, J., Coker, G., Clinton, P.W., Davis, M.R., 2009. Soil quality relationships with tree growth in exotic forests in New Zealand. Forest Ecology and Management 258, 2326-2334. https://doi.org/10.1016/j.foreco.2009.05.026 Watt, M.S., Coker, G., Clinton, P.W., Davis, M.R., Parfitt, R., Simcock, R., Garrett, L., Payn, T., Richardson, B., Dunningham, A., 2005. Defining sustainability of plantation forests through identification of site quality indicators influencing productivity—A national view for New Zealand. Forest Ecology and Management 216, 51-63. https://doi.org/10.1016/j.foreco.2005.05.064 Watt, M.S., Davis, M.R., Clinton, P.W., Coker, G., Ross, C., Dando, J., Parfitt, R.L., Simcock, R., 2008. Identification of key soil indicators influencing plantation productivity and sustainability across a national trial series in New Zealand. Forest Ecology and Management 256, 180-190. https://doi.org/10.1016/j.foreco.2008.04.024 Disclaimer We make no warranties regarding the accuracy or integrity of the Data. We accept no liability for any direct, indirect, special, consequential or other losses or damages of whatsoever kind arising out of access to, or the use of the Data. We are in no way to be held responsible for the use that you put the Data to. You rely on the Data entirely at your own risk.

This 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment

May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.

To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.

Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.

The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.

Arataki - potential impacts of COVID-19 Final Report

Employment modelling - interactive dashboard

The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.

The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).

The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.

Find out more about Arataki, our 10-year plan for the land transport system

May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.

Data reuse caveats: as per license.

Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.

COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]

Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:

a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.

While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.

Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.

As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.

Lineage through time plots (LTTs) are often used to explore past patterns of lineage diversification and community assembly. However, as they are based solely on extant species their ability to accurately depict past events can be questioned. Here, simulation models based on neutral processes are used to explore immigration and extinction scenarios to assist with interpretation of LTT plots generated from molecular clock age estimates for a nearly complete set of extant New Zealand vascular plant genera. With significant genus turnover, model simulations yield smooth exponential and linear semi-log LTT plots. Abrupt changes in extinction and immigration rates imposed in the scenarios caused only slight alterations of the curves. The vast majority of New Zealand vascular plant genera derive from trans-oceanic dispersal, and LTT plots generated from divergence dates and fossil data indicate consistently high turnover. Even major geological and climatic events leave minor signatures on LTT plots. Most notably, the maximum Oligocene Marine Transgression (27–22 Ma), when at least 85% of the current landmass was inundated, isn’t clearly registered. By themselves, LTT plots shed little light on the biotic effects of climatic or geomorphic change in the remote past and need to be interpreted in the light of fossil and geological data.

New Zealand Tsunami Database includes information about tsunamis that have reached the coastline of New Zealand since humans arrived in this land until the present day. The database is primarily the work of historical seismologist Gaye Downes working for GNS Science who collected reports of tsunamis around New Zealand and, in many cases, carried out research to determine parameters of the source, travel time and impact associated with each event. Reports of tsunamis that make up the core of this database come from tide gauges, newspaper articles, harbour masters, records from ships, personal diary entries and Māori oral records. This primary source material is summarised in the database and complete transcriptions are held by the database custodian.

DOI: https://doi.org/10.21420/D6W9-0G74

Cite as: GNS Science. (2020). New Zealand Tsunami Database: Historical and Modern Records [Data set]. GNS Science. https://doi.org/10.21420/D6W9-0G74

Southern right whale population modelThis is the R script used to conduct the population assessments. Two example calls are also given.SRW_SIR_pop_model_REV.rSouthern right whale catch series for New Zealand and Southeast PacificCatch series data, as described in Carroll et al. 2014 (PLoS One 9:e93789), for input into population assessment model.Catch_inputs.csv

There are two different versions of the dataset - one contains the value labels for responses, which will be most useful if you want to analyse in Excel. The other contains numeric values if you want to import into another statistical package.This survey was conducted by researchers in the Department of Geography at the University of Canterbury. We were interested in understanding how the electric scooters, recently introduced in New Zealand, are being used and what respondents think about them. We are interested in perceptions and experiences, whether respondents have used a scooter yourself or not. This is part of a wider project about the environment, health and social implications of new forms of transport.The project was approved by the University of Canterbury Human Ethics Committee (Ref: HEC 2018/49/LR-PS). The survey used a convenience sample as is not a representative population survey. Please read the notes included in the dataset before use and contact the authors with any queriesBoth authors now work at different institutions. Notes for using this dataset:-There are some missing values. We have retained cases where a minimum number of questions were answered, but have not deleted cases listwise so as to preserve potentially useful information. However, please be aware of missing data in analysing this data.-The survey is not representative. A comparison with national datasets will be available shortly, but do not attempt to use this data to represent the attitudes of the population. It is more useful for understanding attitudes of e-scooter users and looking at associations between variables, rather than drawing headline conclusions from one variable.-The sample frame was not a representative population sample. We used convenience sampling through existing networks and social media. This is not a robust or ideal approach to social surveys - however, given time and resourse constraints we opted for an approach that would allow us to collect some early insights into e-scooter use. -We have removed demographic information (other than age and gender) and open-text responses to preserve anonymity. If you require access to these please contact the researchers to discuss.-We suggest contacting us if you are unsure how to interpret the data.-The question numbers and questionnaire is provided.-The questionnaire was conducted online in February and March 2019. -The recommended citation for this dataset is: Curl, A., & Fitt, H. (2019). Attitudes to and use of Electric Scooters in New Zealand Cities [Dataset]. doi.org/10.6084/m9.figshare.8056109

The dataset contains 39148 years of sea level data from 1355 station records, with some stations having alternative versions of the records provided from different sources. GESLA-2 data may be obtained from www.gesla.org. The site also contains the file format description and other information. The text files contain headers with lines of metadata followed by the data itself in a simple column format. All the tide gauge data in GESLA-2 have hourly or more frequent sampling. The basic data from the US National Atmospheric and Oceanic Administration (NOAA) are 6-minute values but for GESLA-2 purposes we instead settled on their readily-available 'verified hourly values'. Most UK records are also hourly values up to the 1990s, and 15-minute values thereafter. Records from some other sources may have different sampling, and records should be inspected individually if sampling considerations are considered critical to an analysis. The GESLA-2 dataset has global coverage and better geographical coverage that the GESLA-1 with stations in new regions (defined by stations in the new dataset located more than 50 km from any station in GESLA-1). For example, major improvements can be seen to have been made for the Mediterranean and Baltic Seas, Japan, New Zealand and the African coastline south of the Equator. The earliest measurements are from Brest, France (04/01/1846) and the latest from Cuxhaven, Germany and Esbjerg, Denmark (01/05/2015). There are 29 years in an average record, although the actual number of years varies from only 1 at short-lived sites, to 167 in the case of Brest, France. Most of the measurements in GESLA-2 were made during the second half of the twentieth century. The most globally-representative analyses of sea level variability with GESLA-2 will be those that focus on the period since about 1970. Historically, delayed-mode data comprised spot values of sea level every hour, obtained from inspection of the ink trace on a tide gauge chart. Nowadays tide gauge data loggers provide data electronically. Data can be either spot values, integrated (averaged) values over specified periods (e.g. 6 minutes), or integrated over a specified period within a longer sampling period (e.g. averaged over 3 minutes every 6 minutes). The construction of this dataset is fundamental to research in sea level variability and also to practical aspects of coastal engineering. One component is concerned with encouraging countries to install tide gauges at locations where none exist, to operate them to internationally agreed standards, and to make the data available to interested users. A second component is concerned with the collection of data from the global set of tide gauges, whether gauges have originated through the GLOSS programme or not, and to make the data available. The records in GESLA-2 will have had some form of quality control undertaken by the data providers. However, the extent to which that control will have been undertaken will inevitably vary between providers and with time. In most cases, no further quality control has been made beyond that already undertaken by the data providers. Although there are many individual contributions, over a quarter of the station-years are provided by the research quality dataset of UHSLC. Contributors include: British Oceanographic Data Centre; University of Hawaii Sea Level Center; Japan Meteorological Agency; US National Oceanic and Atmospheric Administration; Puertos del Estado, Spain; Marine Environmental Data Service, Canada; Instituto Espanol de Oceanografica, Spain; idromare, Italy; Swedish Meteorological and Hydrological Institute; Federal Maritime and Hydrographic Agency, Germany; Finnish Meteorological Institute; Service hydrographique et oc?anographique de la Marine, France; Rijkswaterstaat, Netherlands; Danish Meteorological Institute; Norwegian Hydrographic Service; Icelandic Coastguard Service; Istituto Talassographico di Trieste; Venice Commune, Italy;

The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment. The HLSS30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Sentinel-2A, Sentinel-2B, and Sentinel-2C MSI data products. The HLSS30 and HLSL30 products are gridded to the same resolution and Military Grid Reference System (MGRS) tiling system and thus are “stackable” for time series analysis.The HLSS30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. There are 13 bands included in the HLSS30 product along with four angle bands and a quality assessment (QA) band. See the User Guide for a more detailed description of the individual bands provided in the HLSS30 product.Known Issues Unrealistically high aerosol and low surface reflectance over bright areas: The atmospheric correction over bright targets occasionally retrieves unrealistically high aerosol and thus makes the surface reflectance too low. High aerosol retrievals, both false high aerosol and realistically high aerosol, are masked when quality bits 6 and 7 are both set to 1 (see Table 9 in the User Guide); the corresponding spectral data should be discarded from analysis. Issues over high latitudes: For scenes greater than or equal to 80 degrees north, multiple overpasses can be gridded into a single MGRS tile resulting in an L30 granule with data sensed at two different times. In this same area, it is also possible that Landsat overpasses that should be gridded into a single MGRS tile are actually written as separate data files. Finally, for scenes with a latitude greater than or equal to 65 degrees north, ascending Landsat scenes may have a slightly higher error in the BRDF correction because the algorithm is calibrated using descending scenes. Fmask omission errors: There are known issues regarding the Fmask band of this data product that impacts HLSL30 data prior to April of 2022. The HLS Fmask data band may have omission errors in water detection for cases where water detection using spectral data alone is difficult, and omission and commission errors in cloud shadow detection for areas with great topographic relief. This issue does not impact other bands in the dataset. Inconsistent snow surface reflectance between Landsat and Sentinel-2: The HLS snow surface reflectance can be highly inconsistent between Landsat and Sentinel-2. When assessed on same-day acquisitions from Landsat and Sentinel-2, Landsat reflectance is generally higher than Sentinel-2 reflectance in the visible bands. Unrealistically high snow surface reflectance in the visible bands: By design, the Land Surface Reflectance Code (LaSRC) atmospheric correction does not attempt aerosol retrieval over snow; instead, a default aerosol optical thickness (AOT) is used to drive the snow surface reflectance. If the snow detection fails, the full LaSRC is used in both AOT retrieval and surface reflectance derivation over snow, which produces surface reflectance values as high as 1.6 in the visible bands. This is a common problem for spring images at high latitudes. Unrealistically low surface reflectance surrounding snow/ice: Related to the above, the AOT retrieval over snow/ice is generally too high. When this artificially high AOT is used to derive the surface reflectance of the neighboring non-snow pixels, very low surface reflectance will result. These pixels will appear very dark in the visible bands. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used. In Figure 1, the pixels in front of the glaciers have surface reflectance values that are too low. * Unrealistically low reflectance surrounding clouds: Like for snow, the HLS atmospheric correction does not attempt aerosol retrieval over clouds and a default AOT is used instead. But if the cloud detection fails, an artificially high AOT will be retrieved over clouds. If the high AOT is used to derive the surface reflectance of the neighboring cloud-free pixels, very low surface reflectance values will result. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used. * Unusually low reflectance around other bright land targets: While the HLS atmospheric correction retrieves AOT over non-cloud, non-snow bright pixels, the retrieved AOT over bright targets can be unrealistically high in some cases, similar to cloud or snow. If this unrealistically high AOT is used to derive the surface reflectance of the neighboring pixels, very low surface reflectance values can result as shown in Figure 2. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used. These types of bright targets are mostly man-made, such as buildings, parking lots, and roads. * Dark plumes over water: The HLS atmospheric correction does not attempt aerosol retrieval over water. For water pixels, the AOT retrieved from the nearest land pixels is used to derive the surface reflectance, but if the retrieval is incorrect, e.g. from a cloud pixel, this high AOT will create dark stripes over water, as shown in Figure 3. This happens more often over large water bodies, such as lakes and bays, than over narrow rivers. * Landsat WRS-2 Path/Row boundary in L30 reflectance: HLS performs atmospheric correction on Landsat Level 1 images in the original Worldwide Reference System 2 (WRS2) path/row before the derived surface reflectance is reprojected into Military Grid Reference System (MGRS) tiles. If a WRS-2 Landsat image is very cloudy, the AOT from a few remaining clear pixels might be used for the atmospheric correction of the entire image. The AOT that is used can be quite different from the value for the adjacent row in the same path, which results in an artificial abrupt change from one row to the next, as shown in Figure 4. This occurrence is very rare. * Landsat WRS2 path/row boundary in cloud masks: The cloud mask algorithm Fmask creates mask labels by applying thresholds to the histograms of some metrics for each path/row independently. If two adjacent rows in the same path have distinct distributions within the metrics, abrupt changes in masking patterns can appear across the row boundary, as shown in Figure 5. This occurrence is very rare. * Fmask configuration was deficient for 2-3 months in 2021: The HLS installation of Fmask failed to include auxiliary digital elevation model (DEM) and European Space Agency (ESA) Global Surface Water Occurrence data for a 2-3 month run in 2021. This impacted the masking results over water and in mountainous regions. * The reflectance “scale_factor” and “offset” for some L30 and S30 bands were not set: The HLS reflectance scaling factor is 0.0001 and offset is 0. However, this information was not set in the Cloud Optimized GeoTIFF (COG) files of some bands for a small number of granules. The lack of this information creates a problem for automatic conversion of the reflectance data, requiring explicit scaling in applications. The problem has been corrected, but the affected granules have not been reprocessed. * Incomplete map projection information: For a time, HLS imagery was produced with an incomplete coordinate reference system (CRS). The metadata contains the Universal Transverse Mercator (UTM) zone and coordinates necessary to geolocate pixels within the image but might not be in a standard form, especially for granules produced early in the HLS mission. As a result, an error will occur in certain image processing packages due to the incomplete CRS. The simplest solution is to update to the latest version of Geospatial Data Abstraction Library (GDAL) and/or rasterio, which use the available information without error. * False northing of 10^7 for the L30 angle data: The L30 and S30 products do not use a false northing for the UTM projection, and the angle data are supposed to follow the same convention. However, the L30 angle data incorrectly uses a false northing of 10^7. There is no problem with the angle data itself, but the false northing needs to be set to 0 for it to be aligned with the reflectance. L30 from Landsat L1GT scenes: Landsat L1GT scenes were not intended for HLS due to their poor geolocation. However, some scenes made it through screening for a short period of HLS production. L1GT L30 scenes mainly consist of extensive cloud or snow that can be eliminated using the Fmask quality bits layer. Users can also identify an L1GT-originated L30 granule by examining the HLS cmr.xml metadata file. The UTC dates in the L30/S30 filenames may not be the local dates: UTC dates are used by ESA and the U.S. Geological Survey (USGS) in naming their Level 1 images, and HLS processing retains this information to name the L30 and S30 products. Landsat and Sentinel-2 overpass eastern Australia and New Zealand around 10AM local solar time, but this area is in either UTC+10:00 or +11:00 zone; therefore, the UTC time for some orbits is in fact near the end of the preceding UTC day. For example, HLS.S30.T59HQS.2016117T221552.v2.0 was acquired in the 22nd hour of day 117 of year 2016 in UTC, but the time was 10:15:52 of day 118 locally. Approximately 100 minutes later HLS.S30.T56JML.2016117T235252.v2.0 was acquired in the next orbit in eastern Australia. This issue also occurs for Landsat. For

Preventing child abuse is a unifying goal. Making decisions that affect the lives of children is an unenviable task assigned to social services in countries around the world. The consequences of incorrectly labelling children as being at risk of abuse or missing signs that children are unsafe are well-documented. Evidence-based decision-making tools are increasingly common in social services provision but few, if any, have used social network data. We analyse a child protection services dataset that includes a network of approximately 5 million social relationships collected by social workers between 1996 and 2016 in New Zealand. We test the potential of information about family networks to improve accuracy of models used to predict the risk of child maltreatment. We simulate integration of the dataset with birth records to construct more complete family network information by including information that would be available earlier if these databases were integrated. Including family network data can improve the performance of models relative to using individual demographic data alone. The best models are those that contain the integrated birth records rather than just the recorded data. Having access to this information at the time a child’s case is first notified to child protection services leads to a particularly marked improvement. Our results quantify the importance of a child’s family network and show that a better understanding of risk can be achieved by linking other commonly available datasets with child protection records to provide the most up-to-date information possible.