Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

Basic Information:

Number of entries: 374,661 Number of features: 19 Data Types:

15 integer columns 3 float columns 1 object column (label) Column Names:

id, Time, Is_CH, who CH, Dist_To_CH, ADV_S, ADV_R, JOIN_S, JOIN_R, SCH_S, SCH_R, Rank, DATA_S, DATA_R, Data_Sent_To_BS, dist_CH_To_BS, send_code, Consumed Energy, label Explore the Dataset First Five Rows:

id Time Is_CH who CH Dist_To_CH ADV_S ADV_R JOIN_S JOIN_R SCH_S SCH_R Rank DATA_S DATA_R Data_Sent_To_BS dist_CH_To_BS send_code Consumed Energy label 0 101000 50 1 101000 0.00000 1 0 0 25 1 0 0 0 1200 48 0.00000 1 0.00000 Attack 1 101001 50 0 101044 75.32345 0 4 1 0 0 1 2 38 0 0 0.00000 1 0.09797 Normal 2 101002 50 0 101010 46.95453 0 4 1 0 0 1 19 41 0 0 0.00000 1 0.09797 Normal 3 101003 50 0 101044 64.85231 0 4 1 0 0 1 16 38 0 0 0.00000 1 0.09797 Normal 4 101004 50 0 101010 4.83341 0 4 1 0 0 1 0 41 0 0 0.00000 1 0.09797 Normal Missing Values: No missing values detected in the dataset.

Statistical Summary:

The dataset includes various features related to network operations such as Time, Is_CH, who CH, Dist_To_CH, and more. The target variable label contains categorical values such as "Attack" and "Normal". Analyze Class Distribution Let's analyze the distribution of the classes within the dataset.

python Copy code class_distribution = dataset['label'].value_counts() class_distribution Handle Class Imbalance If a class imbalance is identified, appropriate techniques such as undersampling, oversampling, or SMOTE (Synthetic Minority Over-sampling Technique) will be applied to balance the dataset.

Next Steps:

Identify the class distribution. Apply balancing techniques if necessary. Continue with data preprocessing and feature engineering. We will perform the class distribution analysis and balancing in the subsequent step. ​​

i have found some duplicate values and drop them dataset.duplicated().sum() dataset.drop_duplicates(inplace=True)

ChatGPT Duplicate Handling Initial Duplicate Count Duplicates found: 8,873 Action Taken Duplicates dropped: The dataset has been cleaned by removing all duplicate entries. Verification Duplicates after cleaning: 0 The dataset is now free of duplicate entries. We can proceed with the analysis of the class distribution and any necessary steps to handle class imbalance.

Analyze Class Distribution Let's analyze the distribution of the label column to understand the balance between the classes.

python Copy code class_distribution = dataset['label'].value_counts() class_distribution I'll compute this now. ​​

Class Distribution Analysis The distribution of the classes within the dataset is as follows:

Normal: 332,040 Grayhole: 13,909 Blackhole: 10,049 TDMA: 6,633 Flooding: 3,157 Observations There is a significant class imbalance, with the majority class being "Normal" (332,040 instances). Minority classes include "Grayhole", "Blackhole", "TDMA", and "Flooding".

Computer Junk Cleanup Software Market Outlook

The global market size for computer junk cleanup software was valued at approximately USD 2.4 billion in 2023 and is projected to reach around USD 4.9 billion by 2032, growing at a CAGR of 7.8% during the forecast period. The growth of this market is fueled by increasing digitalization and the expansion of IT infrastructures across various industries, necessitating efficient management of system performance and storage solutions.

One of the primary growth factors for this market is the exponential increase in data generation, which leads to the accumulation of redundant and obsolete files that clutter computer systems. With the rise of big data and the Internet of Things (IoT), organizations are grappling with vast amounts of data, making it essential to employ computer junk cleanup software to optimize system performance and storage. Additionally, the rapid technological advancements in AI and machine learning have enabled more efficient and effective junk cleanup solutions, which further drive market growth.

Another significant factor contributing to market growth is the increasing awareness among individual users and enterprises about the importance of maintaining optimal system performance. As computers and other digital devices are integral to daily operations, both at work and home, ensuring their efficient functioning becomes crucial. Regular use of junk cleanup software helps in enhancing system speed, extending hardware lifespan, and preventing potential security vulnerabilities caused by unnecessary files and software. This awareness is pushing the adoption rate higher across various user segments.

Moreover, the growing trend of remote work and the proliferation of advanced digital devices have made it imperative for organizations to deploy junk cleanup software to maintain system efficiency and security. The shift towards a remote working model necessitates advanced software solutions for performance management and data security, further bolstering the market demand for computer junk cleanup software. Companies are increasingly investing in these solutions to ensure seamless operations, which is amplifying market growth.

In the realm of digital management, Data Cleansing Software plays a pivotal role in ensuring that systems remain efficient and free from unnecessary clutter. As organizations accumulate vast amounts of data, the need for tools that can effectively clean and organize this data becomes paramount. Data Cleansing Software helps in identifying and rectifying errors, removing duplicate entries, and ensuring that the data remains accurate and up-to-date. This not only enhances the performance of computer systems but also supports better decision-making processes by providing clean and reliable data. The integration of such software with junk cleanup solutions can significantly optimize system performance, making it an essential component for enterprises aiming to maintain high standards of data integrity.

From a regional perspective, North America is expected to dominate the computer junk cleanup software market, owing to the high digital literacy rate, robust IT infrastructure, and significant adoption of advanced technologies. However, regions such as Asia Pacific are also witnessing rapid market growth due to the increasing number of small and medium enterprises (SMEs), rising internet penetration, and growing awareness about system optimization and security. Europe follows closely with substantial investments in IT solutions and digital transformation initiatives.

Component Analysis

The computer junk cleanup software market is segmented into software and services. The software segment encompasses standalone applications and integrated system optimization tools that users can install on their devices. This segment is the largest contributor to market revenue, driven by widespread adoption among individual users and enterprises seeking to enhance system performance. These software solutions often come with features such as real-time monitoring, automated cleanup, and advanced algorithms capable of identifying and removing redundant files without compromising essential data.

The services segment, on the other hand, includes professional services, such as system audits, consultancy, installation, and maintenance offered by vendors. This segment is witnessing growth as enterprises increasingly lean on expert services for comprehen

Labour Relations in the United States: 1800An abridged data format, created by Daan Jansen (IISH) and continuing on earlier work by Joris Kok (IISH), is being offered as an alternative in October 2020. This new version of the dataset includes only records that contain labour relations, leaving out all population data. This update also involved (depending on the dataset in question, substantial) data cleaning, separating male and female individuals, and removing any duplicate records. Hence, the aggregated number of people mentioned in these updated datasets should equal the total population.

Labour Relations in Argentina: 1900, 2000

An abridged data format, created by Daan Jansen (IISH) and continuing on earlier work by Joris Kok (IISH), is being offered as an alternative in October 2020. This new version of the dataset includes only records that contain labour relations, leaving out all population data. This update also involved (depending on the dataset in question, substantial) data cleaning, separating male and female individuals, and removing any duplicate records. Hence, the aggregated number of people mentioned in these updated datasets should equal the total population. (2020-11-09)

Duplicate Contact Remover Apps Market Outlook

The global market size for duplicate contact remover apps is poised to experience significant growth, with an estimated valuation of $1.2 billion in 2023, projected to reach $2.8 billion by 2032, reflecting a robust CAGR of 9.5%. The primary growth factors driving this market include the increased adoption of smartphones, the proliferation of digital communication platforms, and the rising demand for efficient contact management solutions to streamline personal and professional communication.

The growth of the duplicate contact remover apps market is propelled largely by the increasing penetration of smartphones across the globe. As smartphones become more integral to daily life, managing contacts efficiently is crucial for both individual and enterprise users. Duplicate contacts can cause confusion, hinder effective communication, and lead to data inconsistency. Hence, there is a growing need for applications that can automatically identify and remove redundant contact entries, ensuring a seamless user experience. Furthermore, the rise in digital communication tools and social media platforms, which often result in multiple entries for the same contact, also contributes to the demand for such apps.

Another significant growth driver is the increasing awareness and emphasis on data cleanliness and accuracy. In an era where data is considered the new oil, maintaining accurate and clean contact databases is vital for effective communication and business operations. Duplicate contacts can lead to miscommunication, missed opportunities, and inefficiencies in customer relationship management (CRM) systems. Businesses are increasingly recognizing the importance of maintaining a clean contact database for improved operational efficiency, driving the adoption of duplicate contact remover apps. Additionally, advancements in AI and machine learning technologies enhance the capabilities of these apps, making them more efficient in identifying and merging duplicate entries.

The surge in remote work and the digital transformation of businesses further fuel the need for effective contact management solutions. With employees working from various locations and relying heavily on digital communication tools, the chances of duplicate contacts increase. Duplicate contact remover apps enable organizations to maintain a unified and accurate contact database, facilitating better communication and collaboration among remote teams. Moreover, the integration of these apps with popular CRM systems and email platforms adds to their utility and adoption, making them an essential tool for modern businesses.

In the realm of innovative solutions for maintaining cleanliness and efficiency, the Automated Facade Contact Cleaning Robot emerges as a groundbreaking technology. This robot is designed to address the challenges associated with cleaning high-rise building facades, which are often difficult and dangerous to maintain manually. By utilizing advanced robotics and automation, these robots can navigate complex surfaces, ensuring thorough cleaning without the need for human intervention. This not only enhances safety but also significantly reduces the time and cost involved in facade maintenance. The integration of such automated solutions is becoming increasingly prevalent in urban environments, where maintaining the aesthetic and structural integrity of buildings is paramount. As cities continue to grow and evolve, the demand for automated cleaning solutions like the Automated Facade Contact Cleaning Robot is expected to rise, offering a glimpse into the future of building maintenance.

Regionally, North America and Europe are expected to lead the market, driven by high smartphone penetration, advanced digital infrastructure, and the presence of major technology companies. Asia Pacific, however, is projected to witness the highest growth rate during the forecast period, owing to the rapid adoption of smartphones, increasing internet penetration, and the growing emphasis on digitalization in emerging economies. The market in Latin America and the Middle East & Africa is also anticipated to grow steadily as awareness about the benefits of contact management solutions increases.

Operating System Analysis

In the context of operating systems, the market for duplicate contact remover apps is segmented into Android, iOS, Windows, and others. The Android segment is expected to dominate the market due to the la

Embark on a transformative journey with our Data Cleaning Project, where we meticulously refine and polish raw data into valuable insights. Our project focuses on streamlining data sets, removing inconsistencies, and ensuring accuracy to unlock its full potential.

Through advanced techniques and rigorous processes, we standardize formats, address missing values, and eliminate duplicates, creating a clean and reliable foundation for analysis. By enhancing data quality, we empower organizations to make informed decisions, drive innovation, and achieve strategic objectives with confidence.

Join us as we embark on this essential phase of data preparation, paving the way for more accurate and actionable insights that fuel success."

Labour Relations in South Africa: 1900, 2000An abridged data format, created by Daan Jansen (IISH) and continuing on earlier work by Joris Kok (IISH), is being offered as an alternative in October 2020. This new version of the dataset includes only records that contain labour relations, leaving out all population data. This update also involved (depending on the dataset in question, substantial) data cleaning, separating male and female individuals, and removing any duplicate records. Hence, the aggregated number of people mentioned in these updated datasets should equal the total population.

This dataset used in this study encompasses a comprehensive collection of records detailing production and advertising.

Key Attributes: Clustering data Date: Timestamp of each record, allowing for temporal analysis. Product_ID: Unique identifier for each product. Production_Quantity: Number of units produced on each day. Advertising_Channel: The medium used for advertising (e.g., social media, television, print). Ad_Spend: Amount of money spent on advertising for each channel. Sales: Number of units sold per day. Customer_Demographics: Information on customer segments, including age, gender, and location. Page_Views: Number of page views generated by the advertisement. Clicks: Number of clicks on the advertisements. Data Preprocessing: To prepare the dataset for analysis, several preprocessing steps were undertaken:

Data Cleaning: Removal of duplicate entries and handling of missing values through imputation. Normalization: Scaling of numerical features to ensure uniformity. Categorical Encoding: Conversion of categorical variables (e.g., Advertising_Channel) into numerical format using one-hot encoding. Usage in Study: This dataset facilitated the development and validation of our budget allocation algorithms and predictive models. By analyzing historical performance data, we were able to derive insights into optimal budget distribution and forecast future advertising impacts on sales and production efficiency.

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used was the electronic telephone directory, available from Swisscom for the Federal Office of Statistics as well as the large social research agencies. The directory contains all registered land-line and mobile telephone numbers.

2,500 randomly selected households were contacted by telephone. The size of the household was recorded and a list of the members of the household noted (with first name, age and gender).

The target respondent, aged 18 years and over, was randomly selected by the computer during the telephone interview.

Mode of data collection

Mail Questionnaire [mail]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

Labour Relations in Italy: 1900, 2000

An abridged data format, created by Daan Jansen (IISH) and continuing on earlier work by Joris Kok (IISH), is being offered as an alternative in October 2020. This new version of the dataset includes only records that contain labour relations, leaving out all population data. This update also involved (depending on the dataset in question, substantial) data cleaning, separating male and female individuals, and removing any duplicate records. Hence, the aggregated number of people mentioned in these updated datasets should equal the total population.

Labour Relations in Japan: 1800, 2000

An abridged data format, created by Daan Jansen (IISH) and continuing on earlier work by Joris Kok (IISH), is being offered as an alternative in October 2020. This new version of the dataset includes only records that contain labour relations, leaving out all population data. This update also involved (depending on the dataset in question, substantial) data cleaning, separating male and female individuals, and removing any duplicate records. Hence, the aggregated number of people mentioned in these updated datasets should equal the total population.