This dataset provides the boundary of New Zealand’s continental shelf, which is the area of seabed around a large land mass where the sea is relatively shallow compared with the open ocean. The continental shelf is the seabed and subsoil of the submarine areas that extend beyond the territorial sea of a coastal state throughout the natural prolongation of its land territory to the outer edge of the continental margin. In New Zealand’s case, the continental margin extends beyond the Exclusive Economic Zone in many places and the outer limits have been established on the basis of the recommendations of the United Nations Commission on the Limits of the Continental Shelf. Under UNCLOS, New Zealand exercises sovereign rights over the continental shelf for the purpose of exploring it and exploiting its natural resources. Note: The boundary includes, where applicable, the delimitation of the boundaries of the continental shelf with Australia under the treaty of 25 July 2004. The delimitation of the maritime boundaries in the north with Fiji, Tonga and possibly France in respect of New Caledonia, have yet to be settled by treaty. Maritime Boundary Definitions: http://www.linz.govt.nz/hydro/nautical-info/maritime-boundaries/definitions#zones Further References: http://www.linz.govt.nz/hydro/nautical-info/maritime-boundaries

This New Zealand Point Cloud Classification Deep Learning Package will classify point clouds into building and background classes. This model is optimized to work with New Zealand aerial LiDAR data.The classification of point cloud datasets to identify Building is useful in applications such as high-quality 3D basemap creation, urban planning, and planning climate change response.Building 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 Building 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.The model is trained with classified LiDAR that follows the 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 6 BuildingApplicable 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 - Auckland, Christchurch, Kapiti, Wellington Testing dataset - Auckland, WellingtonValidation/Evaluation dataset - Hutt City Dataset City Training Auckland, Christchurch, Kapiti, Wellington Testing Auckland, Wellington Validating HuttModel architectureThis model uses the SemanticQueryNetwork model architecture implemented in ArcGIS Pro.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Never Classified 0.984921 0.975853 0.979762 Building 0.951285 0.967563 0.9584Training dataThis model is trained on classified dataset originally provided by Open TopoGraphy with < 1% of manual labelling and correction.Train-Test split percentage {Train: 75~%, Test: 25~%} 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-137.74 m to 410.50 m Number of Returns1 to 5 Intensity16 to 65520 Point spacing0.2 ± 0.1 Scan angle-17 to +17 Maximum points per block8192 Block Size50 Meters Class structure[0, 6]Sample resultsModel to classify a dataset with 23pts/m density Wellington 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

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".

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

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

Flooding occurs regularly in many parts of New Zealand, often resulting in damage and financial loss for building owners. Significant damage occurs upon flood waters entering buildings. An effective method of mitigating flood damage is to elevate building floor levels above design flood levels. This measure can reduce or eliminate the potential damage caused by more frequent (i.e. below design level) flood events. In this Nelson City case study, we analyse how elevating the building finished floor levels can reduce direct economic loss in a modelled Maitai River 1% annual exceedance probability (AEP) flood scenario. RiskScape software is used to model direct economic loss for three impact scenarios; present-day, +0.5m floor levels and +1m floor levels. First finished floor levels are only raised for residential timber frame buildings constructed on pile foundations. For more information about RiskScape software please contact the NIWA project lead, Ryan Paulik (Ryan.Paulik@niwa.co.nz ) or the GNS project lead, Richard Woods (r.woods@gns.cri.nz).

As of June 2017, this dataset has been replaced by the NZ Roads (Addressing) and NZ Roads Subsections (Addressing) datasets and will no longer be updates. See NZ Roads Data Dictionary for field mappings to the new dataset. This source Landonline data exists in order to provide linear geometries against which official road names and street addresses can be recorded. Its purpose is also to enable automated meshblock address reports (for electoral and statistical purposes) so as to identify the presence of a road name in meshblocks where street addresses do not exist. A road centreline in this layer will typically consist of several road centreline geometries from Landonline that have been associated to the same road name. In Landonline a road name is commonly assigned to multiple stretches of roading such as between intersections with other roads. A road name is also commonly duplicated across the country e.g. "Queen Street", "High street" etc. For this layer (as opposed to the Road Subsections Layer. Individual road sections have been aggregated to a single road centreline geometry for each locality. Thus, for example, State Highway 1 has individual sections for differing towns and whenever it crosses a Territorial Authority boundary. Because some roads have multiple names for part of their lengths, some road centreline geometries will duplicate parts of other roads even though there is in reality only one road formation. The road centrelines recorded in Landonline do not represent actual road formation, nor do they represent legal access. They must not be considered as topographic, cadastral, or legal. As a general rule those that are not within a Road Parcels Layer should be treated as being of highly variable accuracy. They do however meet the purposes for which they have been captured, and can provide a valuable resource for other purposes provided that the user(s) understand their origin and consequential limitations. Warning: The purpose of the location field is to enable differentiation between roads of the same name in the dataset. It should not be considered official nor part of a valid postal address. It can however be used as an indicative guide to location within a Territorial Authority, or in the case of regional road networks, as an indicative guide to location within New Zealand.

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

The Commercial Vehicle Safety Programme (CVSP) is a multi-year transformational programme that supports improved road safety. It uses road-side digital & physical infrastructure and centralised systems to record and analyse network heavy vehicle traffic. This supports improved ability to detect heavy vehicle non-compliance and identify at risk behaviour.The data provides heavy vehicle traffic volume count from Commercial Vehicle Safety Centres, grouped byDateTime of the dayCommercial Vehicle Safety Centre nameApproachLaneAxle countVehicle classVehicle categoryMeasured Gross Weight.The data is updated monthly on the 5th of the month.Data reuse caveats: as per license.Data quality statement:Data is sourced from Waka Kotahi New Zealand Transport Agency Commercial Vehicle Safety Programme. In-road and road-side sensors collect data about vehicles continuously. For more information please refer to the Commercial Vehicle Safety Programme webpages. They include information about:Commercial Vehicle Safety Centre locationsHow vehicle screening worksCompliance with Privacy Act 2020Data quality caveats:Data is suitable for statistical analysis, not individual vehicle analysis.Data published is from January 2023.The sensors used for data collection are subject to both technical errors and environmental interference.Only heavy vehicle data is available. In those cases where we could not get a vehicle class from Motor Vehicle Register, we have assumed vehicles over 3.5 t are heavy and we have given them a null vehicle class.To limit the data size, measured gross weights are rounded to the nearest 1000 kg and time of day is split into 4 quarters - '0-6', '7-12', '13-18', '19-24'.Data from Glasnevin CVSC is only published.Published data is summarised and updated monthly.For further information see: Heavy vehicle traffic count data descriptions and vehicle categories

Introduction

Welcome to the New Zealand English Call Center Speech Dataset for the Healthcare domain designed to enhance the development of call center speech recognition models specifically for the Healthcare industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Healthcare domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Healthcare domain call center conversational AI and ASR models for the New Zealand English language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of New Zealand English call center speech recognition models.

Usage and Applications

This dataset can be used for various applications in the fields of speech recognition, natural language processing, and conversational AI, specifically tailored to the Healthcare domain. Potential use cases include:

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

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.2325 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0993 and 0.0612 (in million kms), corressponding to 42.6968% and 26.3068% respectively of the total road length in the dataset region. 0.0721 million km or 30.9965% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0009 million km of information (corressponding to 1.2582% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

This layer includes all Crown Land and Properties managed by LINZ which have been identified spatially and can include properties managed by LINZ on behalf of other agencies. The attributes in this dataset are derived from the National Property and Land Information System (NaPALIS), which is a centralised database for all Land Information New Zealand (LINZ) and Department of Conservation (DOC) administered land. The boundaries of many properties are linked to the applicable Landonline Primary Parcel(s), but in some cases the boundaries may have been drawn in as unsurveyed parcels to varying degrees of accuracy. As such please note that the boundaries are indicative only. The layer excludes any LINZ managed properties which do not have an identified location or extent. More information on Crown Property can be found under the Crown Property section on the LINZ Website. A subset of Crown Property can be found in the South Island Pastoral Leases layer. A table of Property associations to Primary Parcels is published in the LDS here.

Dunedin City in the South Island of New Zealand has many assets and critical infrastructure sitting on a low-lying coastal plain that is underlain by a largely unseen and relatively poorly understood hazard. Shallow groundwater in this area limits the unsaturated ground available to store rain and runoff, promotes flooding and creates opportunities for infiltration into stormwater and wastewater networks. Groundwater levels are expected to rise as sea level rises, causing greater frequency of flooding and/or direct inundation once it nears the ground surface. This zipped archive contains ArcGIS 10.8 geodatabases and spatial analysis of data gathered from a shallow groundwater monitoring network between 6/3/2019 and 1/5/2023. Data are licenced under Creative Commons Attribution 4.0 (CC-BY-4.0) licence without warranty. A series of statistical surfaces represent the present-day (2023) water table elevation and depth to groundwater, the response to rainfall recharge and tidal forcing, the available subsurface storage of rain infiltration. Simple geometric models have also been developed using the present shape and position of the water table, combined with tidal fluctuations, to forecast the future state of groundwater levels at 10 cm increments of sea level rise (up to 1 m). The geometric models are strongly empirical, with many implicit assumptions and caveats – particularly, that they do not account for groundwater flow and possible changes in water-budget mass balance. Although many variables and controlling processes are simplified into a single parameter, the projected groundwater levels highlight how local variations in the water table shape and slope interact locally with the ground elevation or infrastructure networks. They are best considered as a worst-case analysis of groundwater-related contribution to hazard and how this will evolve over time. Further description of these data, and implications from the analysis, can be found in Cox et al. (2023) GNS Science Report 2023/43 doi:10.21420/5799-N894.

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 Protected Area Layer contains land and marine areas, most of which are administered by the Department of Conservation Te Papa Atawhai (DOC) and are protected by the Conservation, Reserves, National Parks, Marine Mammal and Marine Reserves Acts. All of the areas have been identified spatially. The attributes in this dataset are derived from the National Property and Land Information System (NaPALIS), which is a centralised database for all DOC and LINZ administered land.

The boundaries for most protected areas are derived from the Landonline Primary Parcel(s). In some cases, the boundaries may have been based on unsurveyed parcels defined to varying degrees of accuracy. As such please note that the boundaries are indicative only.

The dataset includes reserves but it is not a complete set. Privately owned reserves are excluded from the dataset as they are not crown land. Also, the dataset does not contain a complete list of reserves “vested” in Local Authorities or “controlled and managed” by other organisations. The dataset is continually being updated however as errors or omissions are discovered and new land transactions are completed.

The Department cannot guarantee the accuracy of the information but rather it represents best endeavours to maintain an accurate record of conservation land.

A table of Protected Area associations to Primary Parcels is published in the LDS here.

This Protected Area Layer contains land and marine areas, most of which are administered by the Department of Conservation Te Papa Atawhai (DOC) and are protected by the Conservation, Reserves, National Parks, Marine Mammal and Marine Reserves Acts. All of the areas have been identified spatially. The attributes in this dataset are derived from the National Property and Land Information System (NaPALIS), which is a centralised database for all DOC and LINZ administered land. The boundaries for most protected areas are derived from the Landonline Primary Parcel(s). In some cases, the boundaries may have been based on unsurveyed parcels defined to varying degrees of accuracy. As such please note that the boundaries are indicative only. The dataset includes reserves but it is not a complete set. Privately owned reserves are excluded from the dataset as they are not crown land. Also, the dataset does not contain a complete list of reserves “vested” in Local Authorities or “controlled and managed” by other organisations. The dataset is continually being updated however as errors or omissions are discovered and new land transactions are completed. The Department cannot guarantee the accuracy of the information but rather it represents best endeavours to maintain an accurate record of conservation land. A table of Protected Area associations to Primary Parcels is published in the LDS here.

Lincoln meteorological observations have been taken at a range of sites over the years. A NIWA report (Mullan, A.B; Stuart, S.J; Hadfield, M.G; Smith, M.J (2010). Report on the Review of NIWA's 'Seven-Station' Temperature Series. NIWA Information Series No. 78. pp.129-154) records a number of these along with the work undertaken to reconcile the data between different sites. It is not yet clear which site(s) these measurements were taken at as we have not yet identified a correspondence with NIWA's records.The datasets had been stored as .DAT files. The .DAT files have been uploaded as is, and also standardised and converted into .csv format.Headers: The original .DAT files were stored without headers. Most of these could be recovered for the .csv by running the data through an old program that had been used in conjunction with the data, but one column remains "unknown".Missing data: In the .DAT files, missing measurements are variously recorded, depending on context, as 0, -9, -99 or (in the case of Cloud cover) 9. In the .csv these values have been removed and left blank.Units are most likely:* solar radiation - probably MJ/m2 (megajoules per square metre)* temperatures - Celsius (in early years possibly converted from an original measurement in Fahrenheit)* rainfall - millimetres* cloud - oktas (eighths of the sky taken up by cloud)* wind run - kilometres* vapour pressure - probably Pa (pascals)Lincoln meteorological observations have been taken at a range of sites over the years. A NIWA report (Mullan, A.B; Stuart, S.J; Hadfield, M.G; Smith, M.J (2010). Report on the Review of NIWA's 'Seven-Station' Temperature Series. NIWA Information Series No. 78. pp.129-154) records a number of these along with the work undertaken to reconcile the data between different sites. It is not yet clear which site(s) these measurements were taken at as we have not yet identified a correspondence with NIWA's records.The datasets had been stored as .DAT files. The .DAT files have been uploaded as is, and also standardised and converted into .csv format.Headers: The original .DAT files were stored without headers. Most of these could be recovered for the .csv by running the data through an old program that had been used in conjunction with the data, but one column remains "unknown".Missing data: In the .DAT files, missing measurements are variously recorded, depending on context, as 0, -9, -99 or (in the case of Cloud cover) 9. In the .csv these values have been removed and left blank.Units are most likely:* solar radiation - probably MJ/m2 (megajoules per square metre)* temperatures - Celsius (in early years possibly converted from an original measurement in Fahrenheit)* rainfall - millimetres* cloud - oktas (eighths of the sky taken up by cloud)* wind run - kilometres* vapour pressure - probably Pa (pascals)

Machine learning (ML) models are being widely used in the geosciences for various tasks involving well log data, including prediction of missing well log curves, picking of stratigraphic surfaces, facies classification, and segmentation of different rock types. Even though various ML applications have been proposed in the literature, it is difficult to reproduce and advance the prior art without having access to the data and preprocessing steps used. In fact, there is an increasing need for benchmark cases to assess past and future solutions. The present dataset integrates well log data curated from the 2016 New Zealand Petroleum Exploration Public Data Pack.

Statistical Area 2 2018 (Centroid Inside) is the inside centroid point layer for the major released version of the annually released statistical area 2 (SA2) boundaries for 2018. The "inside" centroid is a point which always falls inside each SA2 polygon as this is often desirable in some users cases. The placement of the point is typically in the widest part of the polygon. The algorithms used are proprietary to ESRI and are related to label placement. Note that the "inside" centroid is NOT always the same as the center of gravity ("true" centroid) of the polygon as in some situations the true centroid may fall outside the Meshblock geometry. The dataset contains the EASTING and NORTHING attributes of the centroid point in NZGD2000 New Zealand Transverse Mercator (EPSG:2193) and LATITUDE and LONGITUDE of the centroid point in decimal degrees in WGS1984 (EPSG:4326) projection.

Please note that a review of SA2 names was undertaken in early 2018. The review addressed issues with inconsistent naming and applied corrections, resulting in an update to this dataset applied in May 2018. All SA2 codes are unchanged.