Shows plots with related information from cemetery database e.g. surname, given names, date of death/burial, age,block number, division name,etc.Data created by joining cemetery plot layer to interment table from cemetery database. Empty or unknown detail plots are not shown in this layer.Data updated daily.

Off-leash areas When exercising dogs off-leash, owners and handlers must maintain control over their dogs at all times. Be alert and make sure that your dog isn't behaving aggressively towards other dogs or people. Dog owners are responsible for cleaning up after their pets.

Fully fenced dog exercise areas Wellington has three fully fenced off-leash dog exercise areas:

lower part of Treasure Island / Kainui Reserve, Hataitai (land opposite Cog Park) Ian Galloway Park in Karori / Northland – this area is lit from dusk until 8pm Taylor Park, Tawa (access through Redwood Station car park).

On-leash areas Leashed dogs are welcome in many popular gardens, most of the Wellington Town Belt and on city streets.

Central city Dog owners are welcome to walk through the central city with their dogs on-leash at all times of the day and throughout the year.

Dogs across the city are not allowed to be left unattended in any public place.

See our: Crash Analysis System (CAS) data user guide

This data comes from the Waka Kotahi Crash Analysis System (CAS), which records all traffic crashes reported to us by the NZ Police. CAS covers crashes on all New Zealand roadways or places where the public have legal access with a motor vehicle.

The data updates monthly, in the first week of each month.

Data is currently available from 1 January 2000. The dataset includes crash variables that are non-personal data.

To give you a quick overview of the data, see the charts in the ‘Attributes’ section below. These will give you information about each of the attributes (variables) in the dataset.

Each chart is specific to a variable, and shows all data (without any filters applied).

Crash Analysis System data - field descriptions

Data reuse caveats: we’ve taken reasonable care in compiling this information, and provide it on an ‘as is, where is’ basis. We're not liable for any action taken on the basis of the information. For further information see the terms of the CC-BY 4.0 International license.

CC-BY 4.0 International licence details

Variables in the dataset are formatted for analytical use. This can result in attribute charts that may not appear meaningful, and are not suitable for broader analysis or use. In addition, some variables aren't mutually exclusive – do not consider them in isolation.

You must not take and use these charts directly as analysis of the overall data.

Data quality statement: we aim to process all fatal crashes within one working day of receiving the crash report from NZ Police.

We aim to process all injury crashes (serious and minor injury) within 4 weeks of receiving the crash report.

It may take up to seven months for non-injury crashes to be processed into CAS.

Up-to-date information on current number of outstanding crash reports

Most unprocessed crash reports will be for crashes where there weren’t any injuries.

Data quality caveats: this data comes from the road traffic crash database Crash Analysis System (CAS) version 2.1.0. As the data is live, data can sometimes change after we receive it – that is, the data is not static after we publish it.

Waka Kotahi NZ Transport Agency maintains the Crash Analysis System. This open data is an appropriately confidentialised version of that.

After a crash, NZ Police send us a Traffic Crash Report (TCR). This may not happen immediately.

A crash must have happened on a road to be recorded in CAS. The CAS definition of a road is any street, motorway or beach, or a place that people can access with a motor vehicle.

There is a lag between the time of a crash to CAS having full and correct crash records. This is due to the police reporting time frame, and data processing.

People don’t report all crashes to the NZ Police. The level of reporting increases with the severity of the crash.

Crash severity is the severity of the worst injury in the crash. There may be more than one injury in a crash.

2020 and 2021 data is incomplete.

For API explorer users, there is a known issue with number-based attribute filters where the “AND” operator is used instead of the “BETWEEN” operator. Substituting “BETWEEN” for “AND” manually in the query URL will resolve this.Update 13/07/2021: previously, there was a 5 month buffer between our internal CAS data and our CAS open data. We have reduced this buffer to 1 month, due to user demand and improved systems.Update 10/12/2020: field type change. The field type for ‘crashFinancialYear’ has changed from integer to text.

Please note: this data differs in some of its columns from the Jan 2013 - Sep 2020 set. This data is a singular snapshot of a dataset published to promote innovation and research. It is not updated or expanded from the date range described.TMS traffic quarter-hourly (2013-2020)

Data reuse caveats: as per license.Data quality statement: please read the accompanying user manual, explaining:how this data is collectedidentification of count stationstraffic monitoring technologymonitoring hierarchy and conventionstypical survey specificationdata calculationTMS operation.Traffic monitoring for state highways: user manual [PDF 465 KB]Data quality caveats: you must use this data in conjunction with the user manual, and the following caveats.Much of this data is sourced from road sensors and may be subject to technical or environmental factors.Data is compiled from a variety of sources. Accuracy may vary and the data should only be used as a guide.Data is representative of the change of traffic volume not of total traffic volume for each region.Data is sourced from Waka Kotahi New Zealand Transport Agency TMS data.Light and heavy traffic volumes have been split using TMS data where vehicles with a length of less than 5.5m are classed as light vehicles. Heavy vehicles are over 11m long. Those between 5.5 and 11m are split 50:50 into light and heavy vehicles.Please be aware that when unzipped these are very large files and some systems may have difficulty opening it. Each file contains more than 10 million rows, and therefore won't work in Excel. However, each file is in CSV format, and will work in:programming languages such as Python or Rdatabases with SQL.State highway traffic monitoring (map)State highway traffic monitoring sitesState highway daily-updated traffic count (API and CSV)

Photo by Joseph Chan on Unsplash

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for New Zealand is 1000.

Mode of data collection

Landline and mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

This metadata record and it's attached files make statements about the kinds of data collected as part of this research, and set out policies for governance of that data, now and in the future. Description: Inflammation is fundamental in maintaining health, and in many healing processes. However, unresolved inflammation has severe health consequences. Unresolved inflammation plays a key role in several chronic diseases, such as cardiovascular disease (CVD), auto-immune diseases such as rheumatoid arthritis, and diabetes and other metabolic-related diseases. Thus, the ability to regulate inflammatory processes is fundamental to maintaining wellbeing. Based on this, the concept of therapeutically targeting systemic inflammation to improve health outcomes is a growing and promising area of research, targeted to improve the wellbeing of people with chronic health disorders.Supplementation with omega-3 has demonstrable effects on lowering biomarkers of inflammation. This trial builds on existing scientific literature demonstrating a role for omega-3 supplementation in improving inflammation.This is a a randomised, placebo-controlled clinical trial to explore the effects of a novel, omega-3-rich food product on markers of inflammation in people with Achilles tendinopathy. Sixty participants will be recruited from physiotherapy clinics across the Auckland region who are presenting with chronic Achilles tendinopathy. Participants will be randomised into treatment and control groups (30/group), and prior to intervention will have blood samples taken for analysis of circulating inflammatory cytokines to study systemic inflammation, take a self-reported questionnaire of tendinopathy severity and undergo ultrasound with elastography to assess symptoms, structure and function of the Achilles tendon, and take questionnaires related to mood and wellbeing.Participants in each group will then receive a food product, with the treatment group product containing the active omega-3 ingredient derived from algal and NZ Hoki oil, and the placebo group product containing a vegetable-derived oil that contains no omega-3. Participants will consume 9g of the food product a day for 12 weeks, which will equate to ~2.5g omega-3/serve in the treatment group. After 6 weeks, and at the end of the study (12 weeks), all outcome measures will be re-assessed.Overall, this trial has the potential to demonstrate a role for omega-3 rich foods in alleviating systemic inflammation, improving musculoskeletal outcomes and improving wellbeing.

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems. By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

The main stock market index in New Zealand (NZX 50) decreased 805 points or 6.14% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from New Zealand. New Zealand Stock Market (NZX 50) - values, historical data, forecasts and news - updated on March of 2025.

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems. By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

This metadata record and it's attached files make statements about the kinds of data collected as part of this research, and set out policies for governance of that data, now and in the future.Description: This trial focused on participants who were of mixed race but who were all pre-diabetic and therefore had a high risk of developing diabetes. As before, the aim was to determine whether or not increased intakes of fructose due to consumption of two kiwifruit per day over a long period could cause harmful metabolic changes in individuals judged by medical criteria as being at high risk of ‘metabolic syndrome’.The third trial ran for three months – enough time for glycosylated haemoglobin (HbA1c) concentrations in the blood to register increased exposure to glycaemia during the trial period. Several clinical biomarkers were measured, as well as plasma Vitamin C. There were no differences between the kiwifruit and control (water) groups in weight gain, blood lipids, HbA1c and other clinical measures, while plasma Vitamin C increased in the kiwifruit group.The results showed that kiwifruit, although containing fruit sugars, may be consumed as an excellent source of Vitamin C, by people at risk of diabetes, without unwanted metabolic side effects.

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

National Coverage.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above. The sample is nationally representative.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in New Zealand was 1,000 individuals.

Mode of data collection

Landline telephone

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

The NZDUSD increased 0.0011 or 0.20% to 0.5749 on Wednesday March 26 from 0.5738 in the previous trading session. New Zealand Dollar - values, historical data, forecasts and news - updated on March of 2025.