This dataset provides values for CORRUPTION INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

According to the Corruption Perception Index, Uruguay was perceived as the least corrupt country in Latin America and the Caribbean in 2024, with a score of 76 out of 100. Venezuela, on the other hand, was found to be the Latin American nation with the worst perceived level of corruption, at 10 points. A role model for Latin American democracy Uruguay has many factors contributing to its low public perception of corruption, from high average income levels to a close-knit urban population. At the forefront is the South American country's adherence to good governance and democracy. In fact, in 2024, Uruguay was ranked as the 13th most democratic country in the world. Going hand in hand with trust in institutions is the prospect of equal opportunities for Uruguayans social advancement. In this area, Uruguay is also ranked as the country in Latin America with the highest social mobility index score. A population in need of reconciliation Corruption has long been an issue souring Latin America. Many experts in the region believe it to be the biggest hindrance to their countries. At the top of this list is Peru, with the largest share of Latin American respondents who think corruption is their country's main problem, followed by Colombia and Brazil. In light of a history of drug trafficking and guerrilla warfare, the number of Colombians who believe that the corrupt elite has captured their political system ranks as the highest in the world. To overcome the consequences of this reputation, the Colombian government has made significant efforts to pass anti-corruption legislation, such as the Colombian Penal Code and the Anti-Corruption Act.

The United States scored 65 points out of 100 on the 2024 Corruption Perceptions Index reported by Transparency International. This dataset provides the latest reported value for - United States Corruption Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In Bolivia, the corruption perception index score reached 28 points in 2024, a slight decrease in comparison to the previous year. This represents less than half of the score recorded that year by the Bahamas, ranked fourth-best corruption perception in Latin America.This index is a composite indicator that includes data on the perception of corruption in areas such as bribery of public officials, kickbacks in public procurement, embezzlement of state funds, and effectiveness of governments' anti-corruption efforts. The worst possible score in perception of corruption is 0, whereas a score of 100 indicates that no corruption is perceived in the respective country.

Vietnam scored 40 points out of 100 on the 2024 Corruption Perceptions Index reported by Transparency International. This dataset provides the latest reported value for - Vietnam Corruption Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The corruption perception index score of Turkey was 34 in 2023, compared with 49 in 2012, implying that corruption levels have grown during this time period. The index itself is a composite indicator that includes data on the perception of corruption in areas such as bribery of public officials, kickbacks in public procurement, embezzlement of state funds, and effectiveness of governments' anti-corruption efforts. The highest possible score in perception of corruption is 0, whereas a score of 100 indicates that no corruption is perceived in the respective country.

This dataset provides values for CORRUPTION RANK reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

In 2023, Denmark was the EU country with the highest corruption perception index score, implying that it is the least corrupt country in the European Union. The other Nordic countries in the EU, Finland and Sweden, also have high scores and are second and third in this statistic respectively. Bulgaria and Hungary have the lowest index score of all EU countries with a score of 45 and 42 respectively. The index itself is a composite indicator that includes data on the perception of corruption in areas such as bribery of public officials, kickbacks in public procurement, embezzlement of state funds, and effectiveness of governments' anti-corruption efforts. The highest possible score in perception of corruption is 0, whereas a score of 100 indicates that no corruption is perceived in the respective country.

In Barbados, the corruption perception index score decreased one point in 2024, after reaching 69 points in 2023. The country has seen better corruption perception levels from 2012 to 2014. Nonetheless, Barbados ranked second as one of the countries with the highest corruption perception in Latin America and the Caribbean. This index is a composite indicator that includes data on the perception of corruption in areas such as bribery of public officials, kickbacks in public procurement, embezzlement of state funds, and effectiveness of governments' anti-corruption efforts. The worst possible score is 0, whereas a score of 100 indicates that no corruption is perceived in the respective country.

Abstract

In 2014 World Bank, with the help of a local survey company, conducted a unique survey in the Kyrgyz Republic surveying 1,080 households in all oblasts (regions) of the country on experience of encountering with corruption practices and attitudes toward social assistance.

The survey is representative at three strata levels: urban, rural and capital city. The questionnaire replicated the set of questions from the official household survey, which allowed to estimate the consumption model and impute the welfare status of the household (i.e. impute the value of per capita consumption expenditure) based on a set of non-monetary/non-consumption questions. As a result it was possible to infer on distributional impact of (petty) corruption practices across different groups of households. Apart from this, the survey collected rich set of data on perception of corruption and household views on corruption practices by various public institutions. The survey greatly assisted informing the anti-corruption strategy and World Bank's dialogue in the country.

Geographic coverage

National

Analysis unit

Respondents aged 16 and older.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary target population is all regular (non-institutional) households in the Kyrgyz Republic. Other target populations are all population oldest 16 years living in non-institutional households. The survey population is identical to the target population; the survey covers all areas within the national borders.

The primary sampling frame will be the list of census enumeration areas (EA) from the Census 2009. There are in all 13 297 enumeration areas in the Republic. Many of the rural EAs are formed around settlements (villages) so the EA coincides with the settlement. Larger settlements contain two or more EAs.

For each EA there is information on total number of people (by sex) and total number of households .There is also administrative information on urban/rural classification, municipality, district and oblast as well as a sketch map of the area. The census maps are kept at the municipality offices. There have been no changes in boundaries between administrative units (municipality, district, and region) since the census, but 50 EAs have been reclassified from urban to rural. The frame has been updated accordingly. The Kyrgyz project employs a stratified two stage design.

The first stage of sampling entails sampling of areas. In the second stage a sample of households are selected in each selected area. The census enumeration areas serve as first stage sampling units - Primary Sampling Units (PSU). Within selected PSUs a sample of households is drawn.

The survey domains are the eight administrative regions plus Bishkek City. It was decided to stratify the sample on survey domain by urban/rural area and Bishkek City. There are therefore 3 strata in all. It was discussed if a deeper stratification- further stratification within region - would give further gains in precision of the estimates. It was concluded that the gains would be small so no further stratification was done. There is, however, an implicit geographical stratification within each province. This is achieved by a geographical ordering of the PSUs and systematic sampling of PSUs in the first stage of sampling.

Number of Sample Households per Cluster

The project had a cluster size of 12households. This is a rather small cluster size compared to what is used in many other surveys focusing on demographic indicators. With a cluster size of 10 households altogether 108 PSUs would be needed to achieve a sample of 1080 households. A larger cluster size than 10 would mean that fewer PSUs than 108are needed. Consequently, the field work costs per household would be lower. On the other hand, the standard errors would be larger. To find out the theoretically "optimum" cluster size - the cluster size that gives the best precision per unit of cost - detailed data on field work costs would be needed. These data are not at hand but some crude calculations could still be done using "guesstimates" on average time for transports between PSUs, time for listing of households in sample PSUs and time for interview per household. Based on the calculations described above it was decided to increase the cluster size from 10to 12 households. This size may well differ from the theoretical optimum value but it is suitable from a practical field work point of view. The total sample size is1080 households. So, the number of PSUs will be 1080/12= 90.

Sampling of PSUs

The PSUs are selected by systematic PPS sampling (PPS= sampling with probabilities proportionate to size). The PSUs are ordered in geographical sequence within the stratum before the selection of PSUs is done. The size measures are the number of households in the PSU from the Census 2009. It was discussed within the sampling group whether there was a need for adjusting the size measures of some PSUs. This should be done in areas where it is known that substantial changes have taken place since the census (e.g. new large scale housing projects or clearance of squatter areas). The conclusion of the discussion was that updating of size measures should not be done. The opinion was that there should be rather few cases of radical population changes and also, that it will be difficult/costly to obtain updated information at the PSU level. The sampling of PSUs will be done in Excel by a standard procedure which is used for all household surveys.

Sampling of Households

For each selected PSU, the starting points will be defined by regional supervisors. Big cities are divided into several territorial units, these units placed in the program Excel, and then the program will randomly select starting points. In the rural area starting point is also determined by supervisors. The interviewer reaches selected from the sample village and then interviewer describes the layout of the streets supervisor and administrative buildings (the number of streets, crossing the street, the number of administrative buildings). This all is entered in the program Excel and then the program will randomly select starting point for this village. The interviewer has no right to select the starting point, or change it.

Starting from the given address/point, an interviewer will follow strict rules to select a household and a respondent within selected household. The random route method using the right-hand rule is used with the predefined interval of three to select the household (counting each third household, excluding the starting point). Never move on the left side! In the deadlock - Interviewers cover only the right side of the street. In apartment building interviewers begin to move from the top floor down in a clockwise direction, also adhere to step 3.

Each third household is considered as main household, where up to three contacts must be attempted at different times of the day, days of the week, and the weekend within the fieldwork period to conduct a successful interview. In areas where the interviewer will not be able to return on a different day, the interviewer will make attempts with at least a two-hour gap between each attempt before substituting the household.

If the interviewer cannot obtain an interview at the main sample household, the interviewer selects the household to the immediate right of the main household as the first substitute. In the event that the attempt at the substitute household also fails, then the interviewer selects the house immediately to the left of the initial/main household as the second substitute. In the event that an interviewer fails to obtain an interview at all three households, the interviewer selects another main household continuing with the same interval and numbering sequence of questionnaires can be saved.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed is such way to replicate the set of questions from official household survey, which eventually allowed to impute the welfare status of household (impute the value of per capita consumption expenditure) based on set of non-monetary/non consumption questions.

Pakistan is the 135 least corrupt nation out of 180 countries, according to the 2024 Corruption Perceptions Index reported by Transparency International. This dataset provides the latest reported value for - Pakistan Corruption Rank - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The 3D Mapping and 3D Modelling Software Market is estimated to be valued at USD 5.16 Billion in 2022 and is expected to reach USD 16.26 Billion by 2030, registering a CAGR of 15.4% during a forecast period of 2023-2030. What are the factors impacting the growth of 3D Mapping and 3D Modelling Software Market?

3D-enabled display devices for advanced and better navigation are increasing the demand for 3D Mapping and 3D Modelling Market 

The increasing need for HD experience is anticipated to spike the development of 3D maps. 3D technology in previous years available to the users was not that satisfactory. Consumers need the finest viewing experience of perceived 3D pictures that look like things. 3D mapping and 3D modeling provide real-life experiences of the surrounding buildings and landscape by seeing them through 3D-enabled devices like tablets, smartphones, and personal computers are projected to rise in the upcoming years. The increasing development in technology, more knowledge of advanced products, and changing lifestyles are surging the demand for 3D-enabled gadgets. Furthermore, the growing need for crisp and realistic picture representation, outstanding 3D effects, and an exceptional mapping and navigation experience is propelling the 3D mapping and 3D modeling market.

Rising corruption and theft concerns are the hurdles to the growth of the 3D Mapping and 3D Modeling Software Market.

The animation industry is still vulnerable to corruption and piracy. Companies' software installations are targeted, and pirated copies are sold on the black market. As a result, the industry suffers massive financial losses. Companies have developed surveillance and monitoring techniques to prevent illicit downloads of 3D mapping and modeling software in order to combat piracy. As a result, people have been encouraged to use lawful digital content. In recent years, government policies and regulatory reforms have been put in place to combat piracy. However, adaptable business plans are required to establish mitigation methods and to take proactive steps such as forming anti-piracy cells and promoting awareness. Moreover, in many countries, there is only one policy to avoid theft is to restrict the sites and penalties to illegal users. Thus, theft is the major hurdle in the growth of the 3D Mapping and 3D Modeling Software Market.

Impact of COVID-19 on the 3D Mapping and 3D Modelling Software Market:

The outbreak of the COVID-19 pandemic has increased the consumer demand for 3D mapping and 3D modeling software. Logistics, online learning, healthcare, e-commerce, and other various online business, collaborations experienced significant expansion, well exceeding the limits of their internal and customer-facing applications. For example, iMap9 is a floor-cleaning robot that can explore and clean floors without the need for human assistance. It uses 3D mapping technology to clean the floors. To manage huge volumes of geographical data while satisfying customer requirements, organizations deploy 3D mapping and modeling software solutions. What is 3D Mapping and 3D Modelling Software?

3D mapping software uses machine vision to help in profiling objects in 3D to map them with the real world, offering the recent technical methods, giving the most advanced technical approaches for visualization and information collecting.3D mapping imaging technology and other plenoptic techniques are also utilized to create the 3D effects by finding the light field. 3D modeling is the method of creating a mathematical representation of a three-dimensional object using software. The resulting product is called a 3D model, and these 3-dimension models are used in various different industries. Increasing demand for 3D animation in mobile applications and the development of 3D-enabled display devices for advanced and better navigation are boosting the growth of the 3D mapping and 3D modeling software market.

This web map shows the location and details of the ICAC Headquarters and Regional Offices in Hong Kong. It is a subset of data made available by the Independent Commission Against Corruption under the Government of Hong Kong Special Administrative Region (the “Government”) at https://portal.csdi.gov.hk ("CSDI Portal"). The source data is processed and converted to Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort. For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong CSDI Portal at https://portal.csdi.gov.hk.

Map showing sea surface temperature quarterly and overall means in the Northern Marine region. The CSIRO Marine Research Remote Sensing facility automatically receives and archives data from the USA's National Oceanographic and Atmospheric Administration (NOAA) satellites. Up to 18 passes per day are tracked to receive data. The Advanced Very High Resolution Radiometer (AVHRR) data is received on the High Resolution Picture Transmission (HRPT) signal. Within an hour of reception, these data are automatically processed into full resolution sea surface temperature (SST) images. Raw data originate from the AVHRR sensor on various NOAA polar orbiting satellites, received at various stations around Australia and consolidated ("stitched") by the CSIRO Earth Observation Centre. The stitching removes redundancy and minimises data corruption. Processing from the stitched archive to produce SST is carried out in the CMR Remote Sensing Facility in Hobart using the split window algorithm of McMillin for NOAA9 and NOAA12 satellites and the NLSST (NOAA non-linear SST) algorithm for the other satellites. Cloud-clearing is performed based on the algorithm of Saunders and Kriebel. Each map is made by combining the estimates over the composite period using a time and spatial neighbourhood median filtering method. Each pixel of the images is the 65 percentile of all cloud-cleared SST estimates during the composite period and within a 4x4 km region. The compositing process also removes most residual cloud contamination. This map has been produced by CSIRO for the National Oceans Office, as part of an ongoing commitment to natural resource planning and management through the 'National Marine Bioregionalisation' project.

The Governance component is formed by the mean of two indicators: the Government effectiveness and the CPI scores. The Government effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies. The indicator shows the effectiveness of the governments’ effort for building resilience across all sectors of society. The CPI scores and ranks countries based on how corrupt a country’s public sector is perceived to be. It is a composite index, a combination of surveys and assessments of corruption, collected by a variety of reputable institutions. The indicator captures the level of misuse of political power for private benefit, which is not directly considered in the construction of the government effectiveness even though interrelated.

The project ‘Truth, Accountability or Impunity? Transitional Justice and the Economic Crisis’ completed a repository of policies of accountability in response to the post-2008 Great Recession in six European countries (Ireland, Iceland, Greece, Cyprus, Portugal & Spain). The repository included recorded prosecutions of bank executives, office holders and politicians on charges related to white collar crimes and/or corruption in the lead up to the economic crisis. It also includes fact finding commissions (i.e. independent commissions of inquiry and/or parliamentary commissions of inquiry) designed to document patterns of policy and institutional failures that led to the economic meltdown, in the period between 2010-2018. The rationale for developing the repository was, first, to map the range of policies deployed and, second, to investigate potential variations in the national policies. In parallel with the development of the repository, the project included the conduct of approximately 133 confidential semi-structured interviews in Ireland, Iceland, Greece, Cyprus, Portugal, Spain, Washington D.C. (IMF) and Brussels (EU). These included interviews with prosecutors, judges, elected officials (e.g. former Prime Ministers, Ministers, MPs), unelected officials (e.g. policymakers at central banks, relevant ministries, EU bodies, senior IMF executives etc), NGO members, journalists, academics, defense lawyers and other informed stakeholders to understand the rationale and their attitudes towards policies of accountability. There is little emphasis in the extant literature on the role and impact of different mechanisms of accountability in post-crisis settings, so these interviews were expected to shed useful analytical light. Finally, with regards to the case selection six European countries with similar background conditions and exposure to the crisis but different policy responses, each representing a different approach to accountability.

The comparative project applies concepts of transitional justice, namely, 'dealing with the past', to investigate how six European societies (Spain, Portugal, Greece, Ireland, Cyprus, Iceland) have come to terms with the origins and consequences of the post-2008 financial crisis. The economic aspects of the crash are well discussed elsewhere; the proposed project argues significant political and legal lessons can be learned from the crisis, but these are missed by viewing it only through an economic lens. Simply stated, transitional justice, a framework developed over the past forty years, considers how national political elites balance popular calls for truth and justice with the pragmatic need for stability in the aftermath of crisis. Prosecutions, truth recovery and amnesties or impunity are much studied mechanisms. Notably, these mechanisms have been deployed in the cases under consideration. Spain and Portugal took only minimal steps to address the causes of the crisis, in effect, pursuing a policy of immunity. Iceland and Cyprus set up ad hoc truth commissions to document the causes of the crisis. Ireland and Greece have prosecuted and convicted a number of bankers and politicians deemed responsible. The project seeks to explain why, despite similar background conditions, societies have formulated different policy responses and to identify the strengths and limitations of each response. This is important. Examining the comparative experience of societies who experiment with policy mechanisms will contribute to the design of better policy responses in times of crisis, decreasing the level of social upheaval, boosting political legitimacy and paving the way for meaningful institutional reform. This project is explicitly about the intersection of politics and law; it focuses on issues of political and institutional failure and the role of law in promoting accountability, responsibility and political learning from economic crises.

In 2024, Colombia's risk of money laundering and terrorist financing added up to 4.92, slightly up from 4.74 reported a year earlier. Still, this signifies a remarkable fall compared to 2019. The Basel AML Index is a composite index, a combination of 16 different indicators with regards to corruption, financial standards, political disclosure and rule of law and tries to measure the risk level of money laundering and terrorist financing in different countries. The numbers used are based on publicly available sources such as the FATF, Transparency International, the World Bank and the World Economic Forum and are meant to serve as a starting point for further investigation.