A document on how to access Fisheries New Zealand’s Commercial Fisheries Management Areas spatial data. This document provide a step-by-step guide to aid you with downloading the updated datasets listed above. You will be able to download the datasets in a variety of format.This document can be found by MPI staff here.

This is a known issue on the MPI Geospatial Platform and is on the backlog for a fix, which will be rectified in 2024.

URL: https://geoscience.data.qld.gov.au/dataset/cr074164

EPM 19238, MPI ROCKS, ANNUAL REPORT FOR PERIOD ENDING 26/10/2012

The Master Patient Index (MPI) software market is experiencing robust growth, driven by the increasing need for accurate and unified patient data across healthcare systems. The market's expansion is fueled by several key factors: the rising adoption of electronic health records (EHRs), the increasing prevalence of chronic diseases requiring comprehensive patient data management, and the growing emphasis on interoperability and data exchange between healthcare providers. Consolidation within the healthcare industry and the demand for improved patient care further contribute to the market's upward trajectory. While precise market sizing data is unavailable, considering a global market for healthcare IT solutions with a similar trajectory, we can estimate the 2025 market size at approximately $2 billion USD, and a Compound Annual Growth Rate (CAGR) of 10% based on conservative projections for the foreseeable future. This growth will be driven primarily by the implementation of advanced features such as data cleansing, deduplication, and real-time patient identification within the MPI systems. Furthermore, increased integration with other healthcare IT systems, including EHRs and patient portals, will drive further market expansion. The competitive landscape includes both established players like McKesson, Oracle, and Epic (inferred based on market presence) and smaller, specialized vendors. These companies are investing heavily in research and development to enhance the functionality and scalability of their MPI solutions. Challenges to growth include high implementation costs, data security concerns, and the complexity of integrating MPI systems with diverse legacy systems across different healthcare settings. Despite these challenges, the long-term outlook remains positive, with continued growth driven by technological advancements, increasing regulatory requirements for data interoperability, and the overarching goal of improving patient safety and care quality. The forecast period of 2025-2033 suggests a significant expansion of this market, with opportunities for both established companies and emerging players alike.

This table describes the marine geospatial data holdings that are available from the Ministry of Primary Industries.This is the underlying table for the which is shown on the open data portal Open data siteHere is the link to the actual catalogue, which can be down loaded.The data can be updated here into this table. it was loaded as requested by the Spatial Intelligence - water team, MPI. Initially the request included the requirement for the team to update the table but this was not followed up and there haven't been any edits to the Catalogue since July 2021.

Bias corrected data of AFRICA CORDEX run, here: MPI-M-MPI-ESM-LR_historical_r1i1p1_UQAM-CRCM5_v1. The bias corrected is done based on the concept of a quantile-quantile mapping using a fitted normal distribution and AgMERRA Input Data; Temporal resolution: daily Spatial resolution: 0.25 DD

A spatial closure to set aside coastal fishing areas which customarily have been of special significance to an iwi or hapu as a source of food (kaimoana) or for spiritual or cultural reasons.This layer is only a geographic representation of Taiapures and contains all states and statuses of a given Taiapure. Only those that have been gazetted are enforceable under law. The full legal descriptionof all gazetted Taiapure can be found here: https://gazette.govt.nz and should be refered to as the single source of truth. The best quality vertices are those that have been located using the Absolute XY tool. All vertices created using other methods may in location by up to 50m.For a more complete description of the layer please go to: http://www.nabis.govt.nz/LayerDetails.aspx?layer=Taiapure

This dataset comprises catch compositions and trawl locations from cruises of Russian fishery research vessels carried out in New Zealand waters between 1964 and 1987, containing 11547 demersal trawls from around New Zealand, Kermadec Islands, Antarctic coast and Tasman Sea. The data were obtained by exchange between the New Zealand Ministry of Fisheries (now Ministry for Primary Industries) and Soviet scientists in the mid 1990s. Note: A peer-review carried out by New Zealand scientists have noted some errors in species identification for some of these records. This Soviet Fishery Data (New Zealand Waters) 1964-1987 dataset is made available under the Creative Commons Attribution 4.0 New Zealand Licence http://creativecommons.org/licenses/by/4.0/nz/. If you publish, distribute, or otherwise disseminate this work to the public without adapting it, the following attribution to MPI should be used: “Source: Ministry for Primary Industries (MPI) and licensed by MPI for re-use under the Creative Commons Attribution 4.0 New Zealand licence." If you adapt this work in any way or include it in a collection, and publish, distribute, or otherwise disseminate that adaptation or collection to the public, the following attribution to MPI should be used: "This work is based on/includes MPI’s data which are licensed by the Ministry for Primary Industries (MPI) for re-use under the Creative Commons Attribution 4.0 New Zealand licence."

This dataset is the land inventory and Land use capability output from the MPI SLMACC Northland project. It is derived from raster data through a series of modelling steps based on field data collected at over 400 observation points. Raster maps are then subjected to segmentation processing to create polygons that are assigned inventory attribute values based on zonal statistics from the original raster datasets. The majority of mapping is carried out by automated processing, the exceptions currently being erosion mapping and parent material (because of scale of available source information). Forestry 300 Index and Net Profit data is derived from modelled surfaces provided by SCION. Land Use Capability is assigned according to a set of rules defined by an expert.

The project report (11 Mb PDF) for this work is downloadable at https://www.mpi.govt.nz/dmsdocument/30615-use-of-modern-technology-including-lidar-to-update-the-new-zealand-land-resource-inventory-report

As of June 28, 2010, the Master Veteran Index (MVI) database based on the enhanced Master Patient Index (MPI) is the authoritative identity service within the VA, establishing, maintaining and synchronizing identities for VA clients, Veterans and beneficiaries. The MVI includes authoritative sources for health identity data and contains over 17 million patient entries populated from all VHA facilities nationwide. The MVI provides the access point mechanism for linking patient's information to enable an enterprise-wide view of patient information, uniquely identifies all active patients who have been admitted, treated, or registered in any VHA facility, and assigns a unique identifier to the patient. The MVI correlates a patient's identity across the enterprise, including all VistA systems and external systems, such as Department of Defense (DoD) and the Nationwide Health Information Network (NwHIN). The MVI facilitates the sharing of health information, resulting in coordinated and integrated health care for Veterans. New Information Technology systems must be interoperable with the MVI and legacy systems will establish integration by October 1, 2012. The Healthcare Identity Management (HC IdM) Team within VHA's Data Quality Program is the steward of patient identity data, performing maintenance and support activities.

This dataset contains data from research trawls that have occurred since 2008 and are stored the NIWA/MPI Trawl database These data results from data collected by research trawl surveys on research vessels and chartered commercial fishing vessels. Trawl surveys are a major tool used by research scientists for stock assessment. They are used to estimate basic parameters of commercial fish populations, including biomass, sex ratio, and the proportion of sexually mature fish, and the distribution of ages and lengths in the population. These parameters may be used in estimating mortality and growth rates.

This dataset is a continuation of data from the NIWA/MPI Trawl database that was initially released as the New Zealand fish and squid distributions from research bottom trawls 1964-2008 available at https://nzobisipt.niwa.co.nz/resource?r=obisprovider

This dataset contains 500 augers observations of soil properties collected for a digital soil mapping and land use capability analysis in the Kaikohe to Paihia area. An additional 172 tacit points of estimated soil properties at sites where pedologists were confident they could predict likely soil distribution are also included.

These data were used in conjunction with elevation, slope and other spatial explicit covariate data to predict soil distributions continuously across 100 km2 of Northland hill country

The project report (11 Mb PDF) for this work is downloadable at https://www.mpi.govt.nz/dmsdocument/30615-use-of-modern-technology-including-lidar-to-update-the-new-zealand-land-resource-inventory-report

NB: data not present in all regions in some layers Aerial Survey Recreational Fishing Efforts - Auckland, Bay of Plenty, Gisborne, Horizons, NMT, Northland, Taranaki, Waikato, Wellington. Coastal Sewer Outfalls - Auckland, Bay of Plenty, Canterbury, Gisborne, Hawke's Bay, Horizons, NMT, Otago, Southland, Taranaki, Wellington, West Coast.Aerial survey recreational fishing efforts: Layer completed by NIWA on behalf of Mfish to spatially describe the relative spatial intensity of recreational fishing effort. Sewer Outfalls: This dataset includes long sea outfalls, near shore outfalls and estuary outfalls that occur around the New Zealand coastline. Other types ofwastewater discharge are not included i.e. groundwater, river, land.

Soil map unit raster classification - generated from a Random Forest model based on soil auger data points and terrain and parent material co-variate layers. This model uses 16 soil map units which represent groupings of taxonomically similar soils at NZ Soil Classification sub-group to family level. Random Forest predicts classifications - the map units so defined will be assigned typical soil associations and properties

The project report (11 Mb PDF) for this work is downloadable at https://www.mpi.govt.nz/dmsdocument/30615-use-of-modern-technology-including-lidar-to-update-the-new-zealand-land-resource-inventory-report

The Major Projects Inventory (MPI), which is published quarterly, lists all major projects that are proposed, planned or underway in British Columbia. These are projects with a capital cost of at least $20 million each within the Lower Mainland and projects valued at $15 million or more in the rest of B.C. This is a point layer. To view the Major Projects Inventory in the BC Economic Atlas click here

The US Global Change Research Program sponsors the semi-annual National Climate Assessment, which is the authoritative analysis of climate change and its potential impacts in the United States. The 4th National Climate Assessment (NCA4), issued in 2018, used high resolution, downscaled LOCA climate data for many of its national and regional analyses. The LOCA downscaling was applied to multi-model mean weighted averages, using the following 32 CMIP5 model ensemble:ACCESS1-0, ACCESS1-3, bcc-csm1-1, bcc-csm1-1-m, CanESM2, CCSM4, CESM1-BGC, CESM1-CAM5, CMCC-CM, CMCC-CMS, CNRM-CM5, CSIRO-Mk3-6-0, EC EARTH, FGOALS-g2, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, GISS-E2-H-p1, GISS-E2-R-p1, HadGEM2-AO, HadGEM2-CC, HadGEM2-ES, inmcm4, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC5, MIROC-ESM-CHEM, MIROC-ESM, MPI-ESM-LR, MPI-ESM-MR, MRI-CGCM3, NorESM1-M.All of the LOCA variables used in NCA4 are presented here. Many are thresholded to provide 47 actionable statistics, like days with precipitation greater than 3", length of the growing season, or days above 90 degrees F. Time RangesStatistics for each variables were calculated over a 30-year period. Four different time ranges are provided:Historical: 1976-2005Early-Century: 2016-2045Mid-Century: 2036-2065Late-Century: 2070-2099Climate ScenariosClimate models use estimates of greenhouse gas concentrations to predict overall change. These difference scenarios are called the Relative Concentration Pathways. Two different RCPs are presented here: RCP 4.5 and RCP 8.5. The number indicates the amount of radiative forcing(watts per meter square) associated with the greenhouse gas concentration scenario in the year 2100 (higher forcing = greater warming). It is unclear which scenario will be the most likely, but RCP 4.5 aligns with the international targets of the COP-26 agreement, while RCP 8.5 is aligns with a more "business as usual" approach. Detailed documentation and the original data from USGCRP, processed by NOAA's National Climate Assessment Technical Support Unit at the North Carolina Institute for Climate Studies, can be accessed from the NCA Atlas. Variable DefinitionsCooling Degree Days: Cooling degree days (annual cumulative number of degrees by which the daily average temperature is greater than 65°F) [degree days (degF)]Consecutive Dry Days: Annual maximum number of consecutive dry days (days with total precipitation less than 0.01 inches)Consecutive Dry Days Jan Jul Aug: Summer maximum number of consecutive dry days (days with total precipitation less than 0.01 inches in June, July, and August)Consecutive Wet Days: Annual maximum number of consecutive wet days (days with total precipitation greater than or equal to 0.01 inches)First Freeze Day: Date of the first fall freeze (annual first occurrence of a minimum temperature at or below 32degF in the fall)Growing Degree Days: Growing degree days, base 50 (annual cumulative number of degrees by which the daily average temperature is greater than 50°F) [degree days (degF)]Growing Degree Days Modified: Modified growing degree days, base 50 (annual cumulative number of degrees by which the daily average temperature is greater than 50°F; before calculating the daily average temperatures, daily maximum temperatures above 86°F and daily minimum temperatures below 50°F are set to those values) [degree days (degF)]growing-season: Length of the growing (frost-free) season (the number of days between the last occurrence of a minimum temperature at or below 32degF in the spring and the first occurrence of a minimum temperature at or below 32degF in the fall)Growing Season 28F: Length of the growing season, 28°F threshold (the number of days between the last occurrence of a minimum temperature at or below 28°F in the spring and the first occurrence of a minimum temperature at or below 28°F in the fall)Growing Season 41F: Length of the growing season, 41°F threshold (the number of days between the last occurrence of a minimum temperature at or below 41°F in the spring and the first occurrence of a minimum temperature at or below 41°F in the fall)Heating Degree Days: Heating degree days (annual cumulative number of degrees by which the daily average temperature is less than 65°F) [degree days (degF)]Last Freeze Day: Date of the last spring freeze (annual last occurrence of a minimum temperature at or below 32degF in the spring)Precip Above 99th pctl: Annual total precipitation for all days exceeding the 99th percentile, calculated with reference to 1976-2005 [inches]Precip Annual Total: Annual total precipitation [inches]Precip Days Above 99th pctl: Annual number of days with precipitation exceeding the 99th percentile, calculated with reference to 1976-2005 [inches]Precip 1in: Annual number of days with total precipitation greater than 1 inchPrecip 2in: Annual number of days with total precipitation greater than 2 inchesPrecip 3in: Annual number of days with total precipitation greater than 3 inchesPrecip 4in: Annual number of days with total precipitation greater than 4 inchesPrecip Max 1 Day: Annual highest precipitation total for a single day [inches]Precip Max 5 Day: Annual highest precipitation total over a 5-day period [inches]Daily Avg Temperature: Daily average temperature [degF]Daily Max Temperature: Daily maximum temperature [degF]Temp Max Days Above 99th pctl: Annual number of days with maximum temperature greater than the 99th percentile, calculated with reference to 1976-2005Temp Max Days Below 1st pctl: Annual number of days with maximum temperature lower than the 1st percentile, calculated with reference to 1976-2005Days Above 100F: Annual number of days with a maximum temperature greater than 100degFDays Above 105F: Annual number of days with a maximum temperature greater than 105degFDays Above 110F: Annual number of days with a maximum temperature greater than 110degFDays Above 115F: Annual number of days with a maximum temperature greater than 115degFTemp Max 1 Day: Annual single highest maximum temperature [degF]Days Above 32F: Annual number of icing days (days with a maximum temperature less than 32degF)Temp Max 5 Day: Annual highest maximum temperature averaged over a 5-day period [degF]Days Above 86F: Annual number of days with a maximum temperature greater than 86degFDays Above 90F: Annual number of days with a maximum temperature greater than 90degFDays Above 95F: Annual number of days with a maximum temperature greater than 95degFTemp Min: Daily minimum temperature [degF]Temp Min Days Above 75F: Annual number of days with a minimum temperature greater than 75degFTemp Min Days Above 80F: Annual number of days with a minimum temperature greater than 80degFTemp Min Days Above 85F: Annual number of days with a minimum temperature greater than 85degFTemp Min Days Above 90F: Annual number of days with a minimum temperature greater than 90degFTemp Min Days Above 99th pctl: Annual number of days with minimum temperature greater than the 99th percentile, calculated with reference to 1976-2005Temp Min Days Below 1st pctl: Annual number of days with minimum temperature lower than the 1st percentile, calculated with reference to 1976-2005Temp Min Days Below 28F: Annual number of days with a minimum temperature less than 28degFTemp Min Max 5 Day: Annual highest minimum temperature averaged over a 5-day period [degF]Temp Min 1 Day: Annual single lowest minimum temperature [degF]Temp Min 32F: Annual number of frost days (days with a minimum temperature less than 32degF)Temp Min 5 Day: Annual lowest minimum temperature averaged over a 5-day period [degF]For For freeze-related variables:The first fall freeze is defined as the date of the first occurrence of 32degF or lower in the nine months starting midnight August 1. Grid points with more than 10 of the 30 years not experiencing an occurrence of 32degF or lower are excluded from the analysis.No freeze occurrence, value = 999The last spring freeze is defined as the date of the last occurrence of 32degF or lower in the nine months prior to midnight August 1. Grid points with more than 10 of the 30 years not experiencing an occurrence of 32degF or lower are excluded from the analysis.No freeze occurrence, value = 999The growing season is defined as the number of days between the last occurrence of 28degF/32degF/41degF or lower in the nine months prior to midnight August 1 and the first occurrence of 28degF/32degF/41degF or lower in the nine months starting August 1. Grid points with more than 10 of the 30 years not experiencing an occurrence of 28degF/32degF/41degF or lower are excluded from the analysis.No freeze occurrence, value = 999

The authoritative source for person identity data. Maintains identity data for persons across VA systems. Provides a unique universal identifier for each person. Stores identity data as correlations for each system where a person is known. Provides a probabilistic matching algorithm. (Includes MPI, PSIM, and IdM TK) Maintains a gold copy known as a Primary View of the persons identity data. Broadcasts identity trait updates to systems of interest. Maintains a record locator service.

A spatial closure for the purposes of helping recognise use and management practices of Mäori in the exercise of non-commercial fishing rights.This layer is only a geographic representation of Mataitai and contains all states and statuses of a given Mataitai. Only those that have been gazetted are enforceable under law. The full legal description of all gazetted Mataitai can be found here: https://gazette.govt.nzand should be refered to as the single source of truth. The best quality vertices are those that have been located using the Absolute XY tool. All vertices created using other methods may vary in location by up to 50m. For a more complete description of the layer please go to: http://www.nabis.govt.nz/LayerDetails.aspx?layer=Mataitai%20Reserve%20Boundaries

This data contains the Fishing Restrictions for a specific fisheries management region. This data was loosely based upon mapping undertaken by the Department of Conservation as published December 2004 in “Area-based restrictions in the New Zealand marine environment”, it has been updated and Quality Assured.NB: data not present in all regions. Scallop Restrictions: Auckland, Bay of Plenty, Canterbury, Northland, Otago, Southland, Waikato. Dredging Restrictions: Southland only. Trawling Over 46m Prohibited: Auckland, Bay of Plenty, Canterbury, Gisborne, Hawke's Bay, Horizons, NMT, Otago, Southland, Taranaki, Waikato, Wellington, West Coast. Trawl Prohibitions: Auckland, Bay of Plenty, Canterbury, Hawke's Bay, NMT, Northland, Otago, Taranaki, Waikato, Wellington. Total Prohibitions: Bay of Plenty, Hawke's Bay, NMT, Northland, Southland. Shellfish Restrictions: Auckland, Bay of Plenty, Canterbury, Gisborne, Hawke's Bay, NMT, Northland, Otago, Southland, Waikato, Wellington. Shellfish Prohibitions: Auckland, Bay of Plenty, Horizons, NMT, Northland, Otago, Taranaki, Waikato, Wellington, West Coast. Set Netting Restrictions: all regions. Set Netting Prohibitions: Auckland, Bay of Plenty, Canterbury, Hawke's Bay, NMT, Northland, Otago, Southland, Taranaki, Waikato, Wellington. Scallop Prohibitions: Auckland, Bay of Plenty, NMT, Northland, Waikato. Oyster Restrictions: Canterbury, NMT, Southland, Wellington, West Coast. Oyster Prohibitions: NMT, Southland, Wellington, West Coast. Drag Netting Restrictions: Auckland, Bay of Plenty, NMT, Northland, Waikato.

This is the National Environmental Standards for Plantation Forestry (NES-PF) Erosion Susceptibility Classification (ESC) and Fish Spawning Indicator (FSI). The ESC and FSI is a tool developed by the Ministry for Primary Industries (MPI) to support councils and foresters in decisions on the level of risk under the NES-PF.

It allows stakeholders to see the erosion susceptibility classification in a given area, plus export breakdowns of this information to pdf.

The WebApp is publicly accessible and can be found here: https://mpi_nes.cloud.eaglegis.co.nz/NESPF/

All data is provided on the MPI open data portal for use in ArcPro desktop applications and down load. Please note the filter needs to be used with download otherwise the download will fail. See here https://data-mpi.opendata.arcgis.com/search?tags=NES%20-%20PF There are currently 14 items 9 of those are data layers. The current datasets are:

Modelled Probability over 50% - Class A (November 2020)

Modelled Probability over 50% - Class B (November 2020)

Modelled Probability over 50% - Class A & B (November 2020)

Non-migratory Fish Spawning Habitats (November 2020)

Fish Spawning Habitats (November 2020)

Erosion Susceptibility Classification (March 2018)