GIS Data Collector Market Outlook

The global GIS Data Collector market size is anticipated to grow from USD 4.5 billion in 2023 to approximately USD 12.3 billion by 2032, at a compound annual growth rate (CAGR) of 11.6%. The growth of this market is largely driven by the increasing adoption of GIS technology across various industries, advances in technology, and the need for effective spatial data management.

An important factor contributing to the growth of the GIS Data Collector market is the rising demand for geospatial information across different sectors such as agriculture, construction, and transportation. The integration of advanced technologies like IoT and AI with GIS systems enables the collection and analysis of real-time data, which is crucial for effective decision-making. The increasing awareness about the benefits of GIS technology and the growing need for efficient land management are also fuelling market growth.

The government sector plays a significant role in the expansion of the GIS Data Collector market. Governments worldwide are investing heavily in GIS technology for urban planning, disaster management, and environmental monitoring. These investments are driven by the need for accurate and timely spatial data to address critical issues such as climate change, urbanization, and resource management. Moreover, regulatory policies mandating the use of GIS technology for infrastructure development and environmental conservation are further propelling market growth.

Another major growth factor in the GIS Data Collector market is the continuous technological advancements in GIS software and hardware. The development of user-friendly and cost-effective GIS solutions has made it easier for organizations to adopt and integrate GIS technology into their operations. Additionally, the proliferation of mobile GIS applications has enabled field data collection in remote areas, thus expanding the scope of GIS technology. The advent of cloud computing has further revolutionized the GIS market by offering scalable and flexible solutions for spatial data management.

Regionally, North America holds the largest share of the GIS Data Collector market, driven by the presence of key market players, advanced technological infrastructure, and high adoption rates of GIS technology across various industries. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, primarily due to rapid urbanization, government initiatives promoting GIS adoption, and increasing investments in smart city projects. Other regions such as Europe, Latin America, and the Middle East & Africa are also experiencing significant growth in the GIS Data Collector market, thanks to increasing awareness and adoption of GIS technology.

The role of a GPS Field Controller is becoming increasingly pivotal in the GIS Data Collector market. These devices are essential for ensuring that data collected in the field is accurate and reliable. By providing real-time positioning data, GPS Field Controllers enable precise mapping and spatial analysis, which are critical for applications such as urban planning, agriculture, and transportation. The integration of GPS technology with GIS systems allows for seamless data synchronization and enhances the efficiency of data collection processes. As the demand for real-time spatial data continues to grow, the importance of GPS Field Controllers in the GIS ecosystem is expected to rise, driving further innovations and advancements in this segment.

Component Analysis

The GIS Data Collector market is segmented by component into hardware, software, and services. Each of these components plays a crucial role in the overall functionality and effectiveness of GIS systems. The hardware segment includes devices such as GPS units, laser rangefinders, and mobile GIS devices used for field data collection. The software segment encompasses various GIS applications and platforms used for data analysis, mapping, and visualization. The services segment includes consulting, training, maintenance, and support services provided by GIS vendors and solution providers.

In the hardware segment, the demand for advanced GPS units and mobile GIS devices is increasing, driven by the need for accurate and real-time spatial data collection. These devices are equipped with high-precision sensors and advanced features such as real-time kinematic (RTK) positioning, which enhance

Market Overview The global Field Data Collection Software market has witnessed tremendous growth in recent years, driven by the increasing demand for real-time data collection and analysis. The market size was estimated to be XXX million in 2025 and is projected to grow at a CAGR of XX% from 2025 to 2033. Key growth drivers include the rising adoption of mobile devices and cloud-based platforms, the need for improved safety and compliance, and the increasing complexity of field operations. Segmentation and Regional Analysis The market is segmented by deployment type (cloud-based and on-premises) and application (environmental, construction, oil and gas, transportation, mining, and others). The environmental segment held the largest market share in 2025, driven by the growing need for environmental monitoring and compliance. Geographically, North America and Europe are the dominant markets, followed by Asia Pacific and the Middle East & Africa. The market in Asia Pacific is expected to witness significant growth in the coming years due to the rapidly expanding construction and mining industries.

Portable Data Collector Market Outlook

The global portable data collector market size was valued at approximately USD 2.5 billion in 2023 and is projected to reach USD 4.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.1% over the forecast period. The growth of this market is primarily driven by the increasing demand for real-time data capture and analysis across various industries. Advancements in technology, such as the integration of IoT and AI, are further propelling the market by enhancing the functionality and efficiency of portable data collectors.

One of the key growth factors for the portable data collector market is the rising need for automation in data collection and processing tasks. Industries such as retail, healthcare, and logistics are increasingly adopting portable data collectors to streamline operations, reduce human errors, and improve overall productivity. These devices enable quick and accurate data capture, which is crucial for inventory management, patient tracking, and supply chain optimization. Additionally, the growing trend of digital transformation across enterprises is encouraging the adoption of advanced data collection solutions.

Another significant factor contributing to the market's growth is the increasing penetration of mobile and wearable technology. The proliferation of smartphones and wearable devices equipped with advanced sensors and connectivity options has made it easier for businesses to deploy portable data collection solutions. These devices offer the flexibility to collect data from remote locations and in real-time, enhancing decision-making processes. Moreover, the integration of cloud computing with portable data collectors allows for seamless data storage and access, further boosting their adoption.

Furthermore, regulatory requirements and standards for data accuracy and security are driving the demand for portable data collectors. Industries such as healthcare and BFSI (Banking, Financial Services, and Insurance) are subject to stringent regulations that mandate precise data capture and secure handling of sensitive information. Portable data collectors equipped with advanced encryption and authentication features are becoming essential tools to comply with such regulations. This trend is expected to continue, further fueling market growth.

From a regional perspective, North America is anticipated to dominate the portable data collector market owing to its advanced technological infrastructure and high adoption rate of innovative solutions. The presence of major market players and the growing emphasis on automation and digitalization in sectors like retail and healthcare are key factors driving the market in this region. Meanwhile, the Asia Pacific region is expected to witness significant growth, attributed to the rapid industrialization and increasing investments in technology by emerging economies like China and India.

Product Type Analysis

The portable data collector market can be segmented by product type into handheld data collectors, wearable data collectors, and mobile data collectors. Handheld data collectors are expected to hold a significant market share, driven by their versatility and ease of use. These devices are widely used in retail, logistics, and healthcare for various applications such as inventory management, asset tracking, and patient care. The robust design and advanced features like barcode scanning and RFID capabilities make handheld data collectors a preferred choice for many industries.

Wearable data collectors are gaining traction due to the increasing adoption of wearable technology in sectors like healthcare and manufacturing. These devices offer hands-free operation, which is particularly beneficial in environments where manual data entry is impractical or hazardous. Wearable data collectors equipped with advanced sensors can monitor and collect data on various parameters such as heart rate, temperature, and movement, making them invaluable in medical and industrial applications. The integration of IoT in wearable data collectors is expected to further enhance their functionality and adoption.

Mobile data collectors, which include smartphones and tablets equipped with data collection apps, are also witnessing substantial growth. The widespread availability of mobile devices and the development of specialized data collection software have made mobile data collectors a cost-effective and flexible solution for businesses. These devices are particularly popular in field data collection activities, where portability a

Field data collection was conducted for the U.S. Army Engineer District, Pacific Ocean, Honolulu (POH), during 23-29 August 2007, in the vicinity of the Natatorium, a World War I memorial in Kapiolani Park, Honolulu, Oahu, Hawaii. Three bottom mounted instruments were deployed to measure waves and currents. A Nortek AWAC (1 MHz) acoustic current profiler was placed seaward of the reef, centered off the Natatorium, in about 5m depth. An RD Instruments ADCP (1.2 MHz) current profiler was mounted on the channel bottom near the entrance, in about 3m depth. The third unit was a Nortek Aquadopp current profiler (2 MHz) was placed in a small hole in the reef, about 35m seaward of the Natatorium pool outer wall in a nominal depth of 1.5m. The first two gauges recorded directional waves and current profiles, the Aquadopp only recorded current profiles. Four inexpensive current drogues (drifters) were designed and built at the CHL Field Research Facility (FRF) that used GPS tracking and radio telemetry for positioning.

Field Device Management (FDM) Market Outlook

The global Field Device Management (FDM) market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach USD 2.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.2% during the forecast period. This significant growth is driven by the increasing demand for seamless integration of field devices with enterprise systems, which enhances operational efficiency and ensures real-time monitoring and control across various industry verticals. The evolution of the Internet of Things (IoT) and Industry 4.0 are central to FDM market dynamics, as they necessitate more sophisticated field device solutions to accommodate the growing complexity and scale of industrial operations.

The burgeoning need for process automation and instrumentation in industries such as oil & gas, chemicals, and pharmaceuticals is a primary growth factor for the FDM market. As these industries strive for higher efficiency and lower operational costs, the necessity for advanced FDM systems becomes more pronounced. These systems not only facilitate device configuration and diagnostics but also provide valuable insights through data analytics. Furthermore, the increasing focus on maintaining safety and compliance with regulatory standards is pushing companies to adopt more robust field device management solutions, ensuring that operations not only meet but exceed required safety protocols. This is particularly critical in sectors dealing with hazardous environments, where even the smallest device malfunction can lead to significant safety risks.

Technological advancements in field devices themselves are also propelling the market forward. As devices become more sophisticated, with features such as enhanced data collection capabilities and connectivity options, the need for equally advanced management systems becomes apparent. These advancements enable the seamless integration of diverse devices into centralized management systems, improving the user interface and user experience. Additionally, the rise of cloud computing has transformed the field device management landscape, offering scalable solutions that can cater to the dynamic needs of businesses. Cloud-based FDM solutions offer the advantage of remote device management, reducing the need for physical presence on-site and enabling more efficient troubleshooting and maintenance operations.

The demand for real-time data analytics and predictive maintenance capabilities is another critical factor contributing to market growth. With the integration of artificial intelligence and machine learning into FDM systems, industries can now predict potential device failures before they occur, thereby minimizing downtime and optimizing operational efficiency. This predictive maintenance capability is increasingly becoming a strategic priority for companies as it allows for the extension of equipment life cycles and reduction in unplanned maintenance costs. Moreover, in highly competitive markets, the ability to leverage real-time data for quick decision-making is a key differentiator. Consequently, companies are investing heavily in FDM systems to maintain their competitive edge and improve overall productivity.

Regionally, the Asia Pacific region is anticipated to witness the highest growth rate, driven by rapid industrialization and increasing investments in automation technologies. With major economies such as China, India, and Japan at the forefront, there is a substantial push towards upgrading industrial infrastructure to enhance productivity and efficiency. Moreover, government initiatives supporting the adoption of advanced technologies in manufacturing and processing industries further bolster the growth prospects for the FDM market in this region. The presence of numerous manufacturing hubs and a growing trend towards digitization across various sectors are expected to sustain this growth trajectory over the forecast period.

Component Analysis

The FDM market is segmented into hardware, software, and services, each playing a crucial role in the comprehensive management of field devices. Hardware components are fundamental as they include all the necessary devices and tools for physical management and data collection from field devices. Innovations in sensors and actuators have significantly enhanced the capability of hardware components, enabling more precise data acquisition and improved field operations. As industries continue to demand higher levels of accuracy and reliability, the hardware segment remains a vital component of FDM solutions, offering the foundational support required for effective man

The MultiTrack Field Recorder market is experiencing robust growth, driven by increasing demand across various applications, particularly in the environmental monitoring, geological surveying, and research sectors. The market's Compound Annual Growth Rate (CAGR) is estimated at 7% from 2025 to 2033, indicating significant expansion potential. This growth is fueled by advancements in recording technology, offering higher fidelity, longer recording times, and enhanced data analysis capabilities. The increasing adoption of remote sensing technologies and the need for precise and reliable data collection in diverse field environments are key drivers. Furthermore, the development of more compact and user-friendly devices is making multitrack field recorders more accessible to a wider range of users. While initial capital investment can be a barrier to entry for some smaller organizations, the long-term cost benefits associated with efficient data acquisition and analysis outweigh this initial hurdle. The market is segmented by application (e.g., environmental monitoring, seismic surveying, acoustic research) and type (e.g., portable, stationary, specialized). North America and Europe currently hold a significant market share, but the Asia-Pacific region is showing rapid growth potential, driven by increasing infrastructure development and research activities. Competition in the market is moderate, with several established players and emerging companies vying for market share through product innovation and strategic partnerships. The forecast period (2025-2033) anticipates continued market expansion, particularly in developing economies. However, factors such as economic fluctuations and potential technological disruptions could impact the market's trajectory. Nevertheless, the ongoing need for accurate and efficient data acquisition in various fields ensures a positive outlook for the MultiTrack Field Recorder market. The increasing integration of advanced analytics and cloud-based data management solutions further enhances the value proposition of these devices, accelerating their adoption and market penetration. Therefore, strategic investments in research and development, alongside targeted marketing efforts focusing on the specific needs of various application sectors, are crucial for success in this growing market.

For full FGDC metadata record, please click here.These data represent Staging and Response Locations collected by GPS for Mississippi, Alabama, and the Florida Panhandle prior to the Deepwater Horizon Oil Spill. The locations for the Peninsular portion of Florida, Georgia, South Carolina, Puerto Rico, and the US Virgin Islands have been compiled from numerous sources into this database schema and will at some later date (after Nov. 2010) be verified and validated by GPS. Staging and response locations were identified first by defining the types of locations that fit these descriptions. The broad categories were defined as Boat Ramp, Marina, Staging Area, or any combination of these. A marina may contain a boat ramp as well as a large parking lot with a seawall suitable for deploying equipment into the water. A staging area may contain just a waterfront park with access to the water, but no boat ramp or marina, but perhaps a dock or pier. These categories and attributes were used to design a specific database schema to collect information on these geographic features that could be used on a GPS-enabled field data collection device. Once the categories of information to be collected and the specifics of what types of information to be collected within each category were determined (the database schema), mobile devices were programmed to accomplish this task and area committee volunteers were used to conduct the field surveys. Field crews were given training on the devices. Guided by base maps identifying potential locations, they then traveled into the field to validate and collect specific GPS and attribute data on those locations. This was a cooperative effort between many federal, state, and local entities guided by FWC-FWRI that resulted in detailed and location-specific information on 366 staging area locations within Sector Mobile and a comprehensive GIS data set that is available on the DVD ROM and website as well a being used in the Geographic Response Plan Map Atlas production. Cyber-Tracker was the software used for this field data collection. Cyber-Tracker is a "shareware" software package developed as a data-capture tool designed for use in Environmental Conservation, Wildlife Biology and Disaster Relief. The software runs on numerous types of mobile devices and designing custom data capture processes for these devices requires no programming experience. Funded in large part by the European Commission and patroned by Harvard University, Cyber-Tracker Software has been a very valuable tool in the data collection efforts of this project. Cyber-Tracker Software can be found on the Internet at: http://www.cybertracker.co.za/.

The rugged handheld device market is experiencing steady growth, projected to reach a market size of $4.866 billion in 2025, with a Compound Annual Growth Rate (CAGR) of 3.3% from 2019 to 2033. This sustained expansion is fueled by increasing demand across diverse sectors such as logistics, manufacturing, field service, and healthcare. The need for durable, reliable, and portable devices capable of withstanding harsh environments and providing real-time data access is a key driver. Furthermore, advancements in technologies like 5G connectivity, enhanced processing power, and improved battery life are contributing to market growth. The integration of advanced features like barcode scanning, GPS tracking, and data analytics further strengthens the appeal of these devices, enabling improved operational efficiency and data-driven decision-making across industries. Competition within the market is robust, with established players like Honeywell, Zebra Technologies, and Datalogic leading the way, alongside emerging players constantly innovating to capture market share. While specific segment breakdowns are unavailable, it is expected that growth will be distributed across various segments reflecting the diverse application of these devices. Looking ahead, the forecast period (2025-2033) anticipates continued growth driven by the ongoing digital transformation across industries. The adoption of rugged handheld devices is expected to accelerate as businesses seek to improve workforce productivity, enhance data security in challenging environments, and gain a competitive edge through improved data management and analysis. While potential restraints, such as economic downturns and the emergence of alternative technologies, could impact the market, the overall trend indicates a positive trajectory for the rugged handheld device sector, ensuring continued growth and innovation throughout the forecast period. Market expansion into developing economies, coupled with ongoing technological advancements, are expected to support this sustained growth.

In 2021–23, the U.S. Geological Survey (USGS), in cooperation with the Ohio Division of Natural Resources, led a study to characterize baseline water quality (2021–23) in eastern Ohio, as they relate to hydraulic fracturing and (or) other oil and gas extraction-related activities. Water-quality data were collected eight times at each of eight sampling sites during a variety of flow conditions to assess baseline water quality. Quality-control (QC) samples collected before and during sampling consisted of blanks and replicates. Blank samples were used to check for contamination potentially introduced during sample collection, processing, equipment cleaning, or analysis. Replicate samples were used to determine the reproducibility or variability in the collection and analysis of environmental samples. All QC samples were collected and processed according to protocols described in the “National Field Manual for the Collection of Water-Quality Data” (USGS, variously dated). To ensure sample integrity and final quality of data, QC samples (one equipment blank, three field blanks, and five replicate samples) were collected for major ions, nutrients, and organics. This data set includes one table of blank samples and one table of field replicate samples. U.S. Geological Survey, variously dated, National field manual for the collection of water-quality data: U.S. Geological Survey Techniques of Water-Resources Investigations, book 9, chaps. A1-A10, available online at http://pubs.water.usgs.gov/twri9A.

This cycle of respective roles in each interview repeats for the two field research assistants on each team using paper (P) or tablet (T) device, and functioning as interviewer (Lead, L) or follower (coder only, F).Four recurring roles for each of the field research assistants over the course of the study.

The global GPS Field Controller market is experiencing robust growth, driven by increasing adoption in surveying, construction, and geological prospecting. The market size in 2025 is estimated at $1.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth is fueled by several key factors. Firstly, advancements in GPS technology are leading to more accurate and efficient data collection, resulting in higher demand for sophisticated field controllers. Secondly, the increasing need for precise spatial data in various industries, coupled with rising infrastructure development globally, is bolstering market expansion. Finally, the integration of advanced features such as touch screen interfaces, improved data processing capabilities, and seamless connectivity with other surveying equipment, is enhancing the overall user experience and productivity, contributing significantly to market growth. However, certain restraints impede market growth. The high initial investment cost of GPS field controllers can be a barrier for small-scale operations. Additionally, the dependence on satellite signals for accurate positioning makes the technology vulnerable to signal interference and atmospheric conditions. Market segmentation reveals a significant preference for touch screen controllers due to their user-friendly interface and ease of operation, while the construction site and geological prospecting applications account for the largest share of market demand. Leading players like Topcon, Trimble, and others are actively engaged in product innovation and strategic partnerships to expand their market presence. The continued technological advancements, increasing infrastructure spending, and a growing awareness of the benefits of precision mapping will contribute to sustained market growth throughout the forecast period. This comprehensive report provides an in-depth analysis of the global GPS Field Controller market, projecting a market valuation exceeding $2 billion by 2028. We delve into market concentration, key trends, dominant segments, and leading companies, offering invaluable insights for stakeholders across the surveying, construction, and geological sectors. This report utilizes rigorous data analysis and expert forecasts to deliver actionable intelligence.

Data Acquisition Device Market Outlook

The global market size for Data Acquisition Devices (DAD) was valued at USD 1.2 billion in 2023 and is anticipated to reach USD 2.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.3% during the forecast period. The increasing demand for high-speed data acquisition systems, coupled with the rapid advancements in sensor technology, is driving the market growth significantly.

One of the key growth factors for the Data Acquisition Device market is the burgeoning need for precise and real-time data in various industry sectors such as automotive, aerospace, and healthcare. The evolution of Industry 4.0 and the increasing reliance on data-driven decision-making processes are further propelling the demand for sophisticated data acquisition solutions. Moreover, the integration of Internet of Things (IoT) technology in industrial applications is amplifying the necessity for advanced data acquisition systems, as these devices are crucial in collecting and transmitting large volumes of data generated by IoT devices.

Additionally, the rising investments in research and development activities by key market players are fostering innovations in data acquisition technologies. Companies are focusing on developing more efficient, accurate, and versatile data acquisition devices that can cater to a wide spectrum of applications. For instance, advancements in wireless data acquisition systems are gaining traction due to their ease of installation and ability to gather data from remote and challenging environments. Furthermore, the increasing adoption of cloud-based data acquisition solutions is enhancing data accessibility and analysis, thus driving market growth.

The healthcare sector is another significant contributor to the growth of the Data Acquisition Device market. Medical institutions are increasingly incorporating sophisticated data acquisition devices to monitor and record patient data in real-time, thereby improving diagnostic accuracy and patient care. The growing trend of telemedicine and remote patient monitoring is also bolstering the demand for these devices. Furthermore, the continuous advancements in wearable technology are providing new opportunities for the data acquisition market, as these wearables generate a vast amount of health-related data that needs to be accurately captured and analyzed.

In the realm of industrial applications, the Portable Industrial Data Analyzer is becoming increasingly significant. These devices offer unparalleled flexibility and ease of use, making them ideal for on-site data collection and analysis. Their compact design allows for easy transportation and deployment in various industrial environments, from manufacturing floors to remote field locations. The integration of advanced features such as wireless connectivity and real-time data processing capabilities enhances their functionality, enabling industries to make informed decisions swiftly. As industries continue to embrace digital transformation, the demand for Portable Industrial Data Analyzers is expected to rise, driven by the need for efficient and accurate data collection solutions.

From a regional perspective, North America held the largest market share in 2023, primarily due to the well-established industrial infrastructure and the presence of major market players in the region. The Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, driven by rapid industrialization, growing investments in smart manufacturing, and increasing adoption of IoT technologies. Europe also represents a significant market for data acquisition devices, owing to the stringent regulations regarding environmental monitoring and the expanding automotive and aerospace sectors.

Component Analysis

The Data Acquisition Device market can be segmented by component into Hardware, Software, and Services. The hardware segment, which includes sensors, actuators, and other data acquisition modules, holds the largest market share. The continuous advancements in sensor technology, such as the development of high-precision and high-speed sensors, are driving the growth of the hardware segment. These sensors are crucial in capturing accurate data in real-time, which is essential for various industrial applications. Additionally, the increasing adoption of wireless sensors is further propelling the demand for hardware components in the data acquisition market.

Analysis of ‘The Bronson Files, Dataset 4, Field 105, 2013’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/392f69f2-aa43-4e90-970d-33c36e011c19 on 11 February 2022.

--- Dataset description provided by original source is as follows ---

Dr. Kevin Bronson provides this unique nitrogen and water management in wheat agricultural research dataset for compute. Ten irrigation treatments from a linear sprinkler were combined with nitrogen treatments. This dataset includes notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, including laboratory analysis results generated during the experimentation, plus high resolution plot level intermediate data tables of SAS process output, as well as the complete raw sensors records and logger outputs.

This data was collected during the beginning time period of our USDA Maricopa terrestrial proximal high-throughput plant phenotyping tri-metric method generation, where a 5Hz crop canopy height, temperature and spectral signature are recorded coincident to indicate a plant health status. In this early development period, our Proximal Sensing Cart Mark1 (PSCM1) platform supplants people carrying the CropCircle (CC) sensors, and with an improved view mechanical performance result.

Experimental design and operational details of research conducted are contained in related published articles, however further description of the measured data signals as well as germane commentary is herein offered.

The primary component of this dataset is the Holland Scientific (HS) CropCircle ACS-470 reflectance numbers. Which as derived here, consist of raw active optical band-pass values, digitized onboard the sensor product. Data is delivered as sequential serialized text output including the associated GPS information. Typically this is a production agriculture support technology, enabling an efficient precision application of nitrogen fertilizer. We used this optical reflectance sensor technology to investigate plant agronomic biology, as the ACS-470 is a unique performance product being not only rugged and reliable but illumination active and filter customizable.

Individualized ACS-470 sensor detector behavior and subsequent index calculation influence can be understood through analysis of white-panel and other known target measurements. When a sensor is held 120cm from a titanium dioxide white painted panel, a normalized unity value of 1.0 is set for each detector. To generate this dataset we used a Holland Scientific SC-1 device and set the 1.0 unity value (field normalize) on each sensor individually, before each data collection, and without using any channel gain boost. The SC-1 field normalization device allows a communications connection to a Windows machine, where company provided sensor control software enables the necessary sensor normalization routine, and a real-time view of streaming sensor data.

This type of active proximal multi-spectral reflectance data may be perceived as inherently “noisy”; however basic analytical description consistently resolves a biological patterning, and more advanced statistical analysis is suggested to achieve discovery. Sources of polychromatic reflectance are inherent in the environment; and can be influenced by surface features like wax or water, or presence of crystal mineralization; varying bi-directional reflectance in the proximal space is a model reality, and directed energy emission reflection sampling is expected to support physical understanding of the underling passive environmental system.

Soil in view of the sensor does decrease the raw detection amplitude of the target color returned and can add a soil reflection signal component. Yet that return accurately represents a largely two-dimensional cover and intensity signal of the target material present within each view. It does however, not represent a reflection of the plant material solely because it can contain additional features in view. Expect NDVI values greater than 0.1 when sensing plants and saturating more around 0.8, rather than the typical 0.9 of passive NDVI.

The active signal does not transmit energy to penetrate, perhaps past LAI 2.1 or less, compared to what a solar induced passive reflectance sensor would encounter. However the focus of our active sensor scan is on the uppermost expanded canopy leaves, and they are positioned to intercept the major solar energy. Active energy sensors are more easy to direct, and in our capture method we target a consistent sensor height that is 1m above the average canopy height, and maintaining a rig travel speed target around 1.5 mph, with sensors parallel to earth ground in a nadir view.

We consider these CropCircle raw detector returns to be more “instant” in generation, and “less-filtered” electronically, while onboard the “black-box” device, than are other reflectance products which produce vegetation indices as averages of multiple detector samples in time.

It is known through internal sensor performance tracking across our entire location inventory, that sensor body temperature change affects sensor raw detector returns in minor and undescribed yet apparently consistent ways.

Holland Scientific 5Hz CropCircle active optical reflectance ACS-470 sensors, that were measured on the GeoScout digital propriety serial data logger, have a stable output format as defined by firmware version.

Different numbers of csv data files were generated based on field operations, and there were a few short duration instances where GPS signal was lost, multiple raw data files when present, including white panel measurements before or after field collections, were combined into one file, with the inclusion of the null value placeholder -9999. Two CropCircle sensors, numbered 2 and 3, were used supplying data in a lined format, where variables are repeated for each sensor, creating a discrete data row for each individual sensor measurement instance.

We offer six high-throughput single pixel spectral colors, recorded at 530, 590, 670, 730, 780, and 800nm. The filtered band-pass was 10nm, except for the NIR, which was set to 20 and supplied an increased signal (including increased noise).

Dual, or tandem, CropCircle sensor paired usage empowers additional vegetation index calculations such as:
DATT = (r800-r730)/(r800-r670)
DATTA = (r800-r730)/(r800-r590)
MTCI = (r800-r730)/(r730-r670)
CIRE = (r800/r730)-1
CI = (r800/r590)-1
CCCI = NDRE/NDVIR800
PRI = (r590-r530)/(r590+r530)
CI800 = ((r800/r590)-1)
CI780 = ((r780/r590)-1)

The Campbell Scientific (CS) environmental data recording of small range (0 to 5 v) voltage sensor signals are accurate and largely shielded from electronic thermal induced influence, or other such factors by design. They were used as was descriptively recommended by the company. A high precision clock timing, and a recorded confluence of custom metrics, allow the Campbell Scientific raw data signal acquisitions a high research value generally, and have delivered baseline metrics in our plant phenotyping program. Raw electrical sensor signal captures were recorded at the maximum digital resolution, and could be re-processed in whole, while the subsequent onboard calculated metrics were often data typed at a lower memory precision and served our research analysis.

Improved Campbell Scientific data at 5Hz is presented for nine collection events, where thermal, ultrasonic displacement, and additional GPS metrics were recorded. Ultrasonic height metrics generated by the Honeywell sensor and present in this dataset, represent successful phenotypic recordings. The Honeywell ultrasonic displacement sensor has worked well in this application because of its 180Khz signal frequency that ranges 2m space. Air temperature is still a developing metric, a thermocouple wire junction (TC) placed in free air with a solar shade produced a low-confidence passive ambient air temperature.

Campbell Scientific logger derived data output is structured in a column format, with multiple sensor data values present in each data row. One data row represents one program output cycle recording across the sensing array, as there was no onboard logger data averaging or down sampling. Campbell Scientific data is first recorded in binary format onboard the data logger, and then upon data retrieval, converted to ASCII text via the PC based LoggerNet CardConvert application. Here, our full CS raw data output, that includes a four-line header structure, was truncated to a typical single row header of variable names. The -9999 placeholder value was inserted for null instances.

There is canopy thermal data from three view vantages. A nadir sensor view, and looking forward and backward down the plant row at a 30 degree angle off nadir. The high confidence Apogee Instruments SI-111 type infrared radiometer, non-contact thermometer, serial number 1052 was in a front position looking forward away from the platform, number 1023 with a nadir view was in middle position, and sensor number 1022 was in a rear position and looking back toward the platform frame, until after 4/10/2013 when the order was reversed. We have a long and successful history testing and benchmarking performance, and deploying Apogee Instruments infrared radiometers in field experimentation. They are biologically spectral window relevant sensors and return a fast update 0.2C accurate average surface temperature, derived from what is (geometrically weighted) in their field of view.

Data gaps do exist beyond null value -9999 designations, there are some instances when GPS signal was lost, or rarely on HS GeoScout logger error. GPS information may be missing at the start of data recording.

The dataset to be published was generated through exploratory case studies conducted on wrist-worn devices from three vendors: Huawei, Amazfit, and Xiaomi. The specific devices investigated include the Huawei Fit 2 Smartwatch and Band 7, Amazfit Band 7, and Xiaomi Watch 3. These devices operate on different operating systems, namely Android Wear, Zepp OS, and Wear OS.

The data collection period for each device varies, with Huawei having approximately one year of data collected, while the other devices have shorter durations. All wrist-wear devices from different vendors were connected to an iPhone 11 mobile device, which acted as the host device. The iPhone facilitated data synchronization and provided access to the data through the respective health applications provided by the vendors.

To extract the data, MD-NEXT was employed, and the extracted data was further analyzed using the MD-RED tool. These tools were chosen due to their recognized forensically sound capabilities. As a result, the dataset contains data that is considered suitable for use in digital forensics fields.

Overall, the dataset provides valuable information obtained from wrist-worn devices, covering multiple vendors, operating systems, and data collection periods. Researchers in the digital forensics field can utilize this dataset for various investigative and analytical purposes.

The global GPS Field Controller market is experiencing robust growth, driven by increasing adoption in construction, geological prospecting, and other surveying applications. Technological advancements, such as improved accuracy, enhanced connectivity, and the integration of advanced features like real-time kinematic (RTK) capabilities, are fueling market expansion. The market is segmented by controller type (touch screen, full keyboard, other) and application, with construction and geological prospecting representing significant segments. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This growth trajectory is fueled by rising infrastructure development globally, increasing demand for precise land surveying, and the burgeoning adoption of digital technologies within these sectors. Leading players like Topcon, CHC Navigation, GeoMax, Hi-Target Surveying Instrument, Sokkia, and Trimble are actively shaping market dynamics through continuous product innovation and strategic partnerships. Growth is further propelled by the increasing need for efficient data collection and processing in the field. The shift towards automation and data-driven decision-making in surveying and construction enhances the appeal of GPS field controllers. However, factors like the high initial investment cost and the requirement for skilled personnel to operate the equipment could act as restraints. Nevertheless, the long-term benefits of increased productivity and accuracy are expected to outweigh these limitations, ensuring the continued expansion of this market. Regional variations exist, with North America and Europe expected to dominate the market share due to high adoption rates and technological advancements. However, rapidly developing economies in the Asia-Pacific region are likely to exhibit significant growth in the coming years.

Introduction

The 802.11 standard includes several management features and corresponding frame types. One of them are probe requests (PR). They are sent by mobile devices in the unassociated state to search the nearby area for existing wireless networks. The frame part of PRs consists of variable length fields called information elements (IE). IE fields represent the capabilities of a mobile device, such as data rates.
The dataset includes PRs collected in a controlled rural environment and in a semi-controlled indoor environment under different measurement scenarios.
It can be used for various use cases, e.g., analysing MAC randomization, determining the number of people in a given location at a given time or in different time periods, analysing trends in population movement (streets, shopping malls, etc.) in different time periods, etc.

Measurement setup

The system for collecting PRs consists of a Raspberry Pi 4 (RPi) with an additional WiFi dongle to capture Wi-Fi signal traffic in monitoring mode. Passive PR monitoring is performed by listening to 802.11 traffic and filtering out PR packets on a single WiFi channel.
The following information about each PR received is collected: MAC address, Supported data rates, extended supported rates, HT capabilities, extended capabilities, data under extended tag and vendor specific tag, interworking, VHT capabilities, RSSI, SSID and timestamp when PR was received.
The collected data was forwarded to a remote database via a secure VPN connection. A Python script was written using the Pyshark package for data collection, preprocessing and transmission.

Data preprocessing

The gateway collects PRs for each consecutive predefined scan interval (10 seconds). During this time interval, the data are preprocessed before being transmitted to the database.
For each detected PR in the scan interval, IEs fields are saved in the following JSON structure:
PR_IE_data =
{
'DATA_RTS': {'SUPP': DATA_supp , 'EXT': DATA_ext},
'HT_CAP': DATA_htcap,
'EXT_CAP': {'length': DATA_len, 'data': DATA_extcap},
'VHT_CAP': DATA_vhtcap,
'INTERWORKING': DATA_inter,
'EXT_TAG': {'ID_1': DATA_1_ext, 'ID_2': DATA_2_ext ...},
'VENDOR_SPEC': {VENDOR_1:{
'ID_1': DATA_1_vendor1,
'ID_2': DATA_2_vendor1
...},
VENDOR_2:{
'ID_1': DATA_1_vendor2,
'ID_2': DATA_2_vendor2
...}
...}
}

Supported data rates and extended supported rates are represented as arrays of values that encode information about the rates supported by a mobile device. The rest of the IEs data is represented in hexadecimal format. Vendor Specific Tag is structured differently than the other IEs. This field can contain multiple vendor IDs with multiple data IDs with corresponding data. Similarly, the extended tag can contain multiple data IDs with corresponding data.
Missing IE fields in the captured PR are not included in PR_IE_DATA.

When a new MAC address is detected in the current scan time interval, the data from PR is stored in the following structure:

{'MAC': MAC_address, 'SSIDs': [ SSID ], 'PROBE_REQs': [PR_data] },

where PR_data is structured as follows:
{
'TIME': [ DATA_time ],
'RSSI': [ DATA_rssi ],
'DATA': PR_IE_data
}.

This data structure allows storing only TOA and RSSI for all PRs originating from the same MAC address and containing the same PR_IE_data. All SSIDs from the same MAC address are also stored.
The data of the newly detected PR is compared with the already stored data of the same MAC in the current scan time interval.
If identical PR's IE data from the same MAC address is already stored, then only data for the keys TIME and RSSI are appended.
If no identical PR's IE data has yet been received from the same MAC address, then PR_data structure of the new PR for that MAC address is appended to PROBE_REQs key.
The preprocessing procedure is shown in Figure ./Figures/Preprocessing_procedure.png
At the end of each scan time interval, all processed data is sent to the database along with additional metadata about the collected data e.g. wireless gateway serial number and scan start and end timestamps. For an example of a single PR captured, see the ./Single_PR_capture_example.json file.

Environments description

We performed measurements in a controlled rural outdoor environment and in a semi-controlled indoor environment of the Jozef Stefan Institute.
See the Excel spreadsheet Measurement_informations.xlsx for a list of mobile devices tested.

Indoor environment

We used 3 RPi's for the acquisition of PRs in the Jozef Stefan Institute. They were placed indoors in the hallways as shown in the ./Figures/RPi_locations_JSI.png. Measurements were performed on weekend to minimize additional uncontrolled traffic from users' mobile devices. While there is some overlap in WiFi coverage between the devices at the location 2 and 3, the device at location 1 has no overlap with the other two devices.

Rural environment outdoors

The three RPi's used to collect PRs were placed at three different locations with non-overlapping WiFi coverage, as shown in ./Figures/RPi_locations_rural_env.png. Before starting the measurement campaign, all measured devices were turned off and the environment was checked for active WiFi devices. We did not detect any unknown active devices sending WiFi packets in the RPi's coverage area, so the deployment can be considered fully controlled.
All known WiFi enabled devices that were used to collect and send data to the database used a global MAC address, so they can be easily excluded in the preprocessing phase. MAC addresses of these devices can be found in the ./Measurement_informations.xlsx spreadsheet.
Note: The Huawei P20 device with ID 4.3 was not included in the test in this environment.

Scenarios description

We performed three different scenarios of measurements.

Individual device measurements

For each device, we collected PRs for one minute with the screen on, followed by PRs collected for one minute with the screen off. In the indoor environment the WiFi interfaces of the other devices not being tested were disabled. In rural environment other devices were turned off. Start and end timestamps of the recorded data for each device can be found in the ./Measurement_informations.xlsx spreadsheet under the Indoor environment of Jozef Stefan Institute sheet and the Rural environment sheet.

Three groups test

In this measurement scenario, the devices were divided into three groups. The first group contained devices from different manufacturers. The second group contained devices from only one manufacturer (Samsung). Half of the third group consisted of devices from the same manufacturer (Huawei), and the other half of devices from different manufacturers. The distribution of devices among the groups can be found in the ./Measurement_informations.xlsx spreadsheet.

The same data collection procedure was used for all three groups. Data for each group were collected in both environments at three different RPis locations, as shown in ./Figures/RPi_locations_JSI.png and ./Figures/RPi_locations_rural_env.png.
At each location, PRs were collected from each group for 10 minutes with the screen on. Then all three groups switched locations and the process was repeated. Thus, the dataset contains measurements from all three RPi locations of all three groups of devices in both measurement environments. The group movements and the timestamps for the start and end of the collection of PRs at each loacation can be found in spreadsheet ./Measurement_informations.xlsx.

One group test

In the last measurement scenario, all devices were grouped together. In rural evironement we first collected PRs for 10 minutes while the screen was on, and then for another 10 minutes while the screen was off. In indoor environment data were collected at first location with screens on for 10 minutes. Then all devices were moved to the location of the next RPi and PRs were collected for 5 minutes with the screen on and then for another 5 minutes with the screen off.

Folder structure

The root directory contains two files in JSON format for each of the environments where the measurements took place (Data_indoor_environment.json and Data_rural_environment.json). Both files contain collected PRs for the entire day that the measurements were taken (12:00 AM to 12:00 PM) to get a sense of the behaviour of the unknown devices in each environment. The spreadsheet ./Measurement_informations.xlsx. contains three sheets. Devices description contains general information about the tested devices, RPis, and the assigned group for each device. The sheets Indoor environment of Jozef Stefan Institute and Rural environment contain the corresponding timestamps for the start and end of each measurement scenario. For the scenario where the devices were divided into groups, additional information about the movements between locations is included. The location names are based on the RPi gateway ID and may differ from those on the figures showing the

This is a video demonstrating how to connect Collector for ArcGIS to an external GNSS receiver.Steps:Connect your mobile device to the external GNSS receiver using bluetooth.Once the connection is successful, open an ArcGIS mobile app for field data collection (e.g., Collector for ArcGIS).Go to Settings, and look for Location setting.Press "Provider", click the add ("+") button, and choose the appropriate external GNSS receiver.You can specify the antenna height, if applicable, and then press "Done".The Collector for ArcGIS can now be used to collect field data by utilising the connected external GNSS receiver.Credits: Anatum GeoMobile Solutions

Snapshot img

Land Surveying Equipment Market Size 2025-2029

The land surveying equipment market size is forecast to increase by USD 2.95 billion, at a CAGR of 6.3% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for accurate mapping and data analysis in various industries, including construction, engineering, and real estate. This trend is driving the adoption of advanced surveying technologies, such as robotic total stations and 3D scanners, which offer higher precision and efficiency compared to traditional methods. Robotic total stations, in particular, are gaining popularity due to their ability to automate the surveying process, reducing human error and increasing productivity. These devices use GPS technology and self-leveling mechanisms to capture data and generate accurate surveys. However, the market faces challenges in the form of regulatory and compliance requirements. As surveying data is critical for infrastructure projects, governments and regulatory bodies impose stringent regulations to ensure data accuracy and security. Compliance with these regulations can be time-consuming and costly, posing a significant challenge for market players. Moreover, the emergence of drone technology in surveying applications is another trend transforming the market landscape. Drones equipped with high-resolution cameras and LiDAR sensors are increasingly being used for topographic surveys, volumetric analysis, and infrastructure inspections. However, the use of drones in surveying raises concerns regarding data privacy, security, and safety, which need to be addressed through regulatory frameworks and technological solutions. In conclusion, the market is poised for growth due to the increasing demand for accurate mapping and data analysis. The adoption of advanced surveying technologies, such as robotic total stations and drones, is driving innovation and efficiency in the market. However, regulatory and compliance challenges, data privacy concerns, and safety issues pose significant obstacles that market players need to navigate effectively to capitalize on market opportunities and maintain a competitive edge.

What will be the Size of the Land Surveying Equipment Market during the forecast period?

Request Free SampleThe market is characterized by continuous evolution and dynamic market activities. Construction staking and cadastral surveying remain key applications, with data integrity and report generation playing essential roles in ensuring accuracy and reliability. Data processing, survey software, and RTK systems facilitate efficient data collection and analysis, while total stations and control surveys ensure alignment and precision. Alignment surveys and as-built surveys are crucial for infrastructure development, as are elevation surveys and field data collection in construction sites. Aerial surveying, site plans, and BIM integration are transforming the industry with advanced technologies such as laser scanning and drone surveying. Mining surveying, land development, and engineering surveying require high levels of data analysis and GIS integration for effective planning and execution. Hydrographic surveying, ground control points, and coordinate systems ensure data accuracy in various applications. Emerging technologies like artificial intelligence, machine learning, and point cloud processing are revolutionizing the industry, offering new possibilities for data analysis and automation. The market's ongoing unfolding is marked by the integration of GPS mapping, infrastructure development, and environmental monitoring, among others. Construction surveying, site preparation, and boundary surveys are essential components of real estate development, with 3D modeling and GNSS receivers streamlining the process. The market's continuous evolution underscores the importance of staying updated with the latest trends and technologies.

How is this Land Surveying Equipment Industry segmented?

The land surveying equipment industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ProductTS and TLUAVGNSS systemPipe lasersOthersEnd-userConstructionMiningOil and gasOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKAPACAustraliaChinaIndiaJapanSouth AmericaBrazilRest of World (ROW)

By Product Insights

The ts and tl segment is estimated to witness significant growth during the forecast period.The market experiences significant growth due to the increasing adoption of advanced technologies in surveying applications. Total stations and theodolites, such as TS and TL, play a pivotal role in this market. These instruments, which measure angles, distances, and elevations with high precision, are indispensable in construction, infrastr

The dataset to be published was generated through exploratory case studies conducted on wrist-worn devices from three vendors: Huawei, Amazfit, and Xiaomi. The specific devices investigated include the Huawei Fit 2 Smartwatch and Band 7, Amazfit Band 7, and Xiaomi Watch 3. These devices operate on different operating systems, namely Android Wear, Zepp OS, and Wear OS.

The data collection period for each device varies, with Huawei having approximately one year of data collected, while the other devices have shorter durations. All wrist-wear devices from different vendors were connected to an iPhone 11 mobile device, which acted as the host device. The iPhone facilitated data synchronization and provided access to the data through the respective health applications provided by the vendors.

To extract the data, MD-NEXT was employed, and the extracted data was further analyzed using the MD-RED tool. These tools were chosen due to their recognized forensically sound capabilities. As a result, the dataset contains data that is considered suitable for use in digital forensics fields.

Overall, the dataset provides valuable information obtained from wrist-worn devices, covering multiple vendors, operating systems, and data collection periods. Researchers in the digital forensics field can utilize this dataset for various investigative and analytical purposes.

Abstract

The Palestinian society's access to information and communication technology tools is one of the main inputs to achieve social development and economic change to the status of Palestinian society; on the basis of its impact on the revolution of information and communications technology that has become a feature of this era. Therefore, and within the scope of the efforts exerted by the Palestinian Central Bureau of Statistics in providing official Palestinian statistics on various areas of life for the Palestinian community, PCBS implemented the household survey for information and communications technology for the year 2023. The main objective of this report is to present the trends of accessing and using information and communication technology by households and individuals in Palestine, and enriching the information and communications technology database with indicators that meet national needs and are in line with international recommendations.

Geographic coverage

Palestine, West Bank, Gaza strip

Analysis unit

Household, Individual

Universe

All Palestinian households and individuals (10 years and above) whose usual place of residence in 2023 was in the state of Palestine.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The sampling frame consists of master sample which were enumerated in the 2017 census. Each enumeration area consists of buildings and housing units with an average of about 150 households. These enumeration areas are used as primary sampling units (PSUs) in the first stage of the sampling selection.

Sample Size The sample size is 8,040 households.

Sampling Design The sample is three stages stratified cluster (pps) sample. The design comprised three stages: Stage (1): Selection a stratified sample of 536 enumeration areas with (pps) method. Stage (2): Selection a stratified random sample of 15 households from each enumeration area selected in the first stage. Stage (3): Selection one person of the (10 years and above) age group in a random method by using KISH TABLES.

Sample Strata The population was divided by: 1- Governorate (16 governorates, where Jerusalem was considered as two statistical areas) 2- Type of Locality (urban, rural, camps).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaire The survey questionnaire consists of identification data, quality controls and three main sections: Section I: Data on household members that include identification fields, the characteristics of household members (demographic and social) such as the relationship of individuals to the head of household, sex, date of birth and age.

Section II: Household data include information regarding computer processing, access to the Internet, and possession of various media and computer equipment. This section includes information on topics related to the use of computer and Internet, as well as supervision by households of their children (5-17 years old) while using the computer and Internet, and protective measures taken by the household in the home.

Section III: Data on Individuals (10 years and above) about computer use, access to the Internet, possession of a mobile phone, information threats, and E-commerce.

Cleaning operations

Field Editing and Supervising

• Data collection and coordination were carried out in the field according to the pre-prepared plan, where instructions, models and tools were available for fieldwork. • Audit process on the PC-Tablet is through the establishment of all automated rules and the office on the program to cover all the required controls according to the criteria specified. • For the privacy of Jerusalem (J1) data were collected in a paper questionnaire. Then the supervisor verifies the questionnaire in a formal and technical manner according to the pre-prepared audit rules. • Fieldwork visits was carried out by the project coordinator, supervisors and project management to check edited questionnaire and the performance of fieldworkers.

Data Processing

Programming Consistency Check The data collection program was designed in accordance with the questionnaire's design and its skips. The program was examined more than once before the conducting of the training course by the project management where the notes and modifications were reflected on the program by the Data Processing Department after ensuring that it was free of errors before going to the field.

Using PC-tablet devices reduced data processing stages, and fieldworkers collected data and sent it directly to server, and project management withdraw the data at any time.

In order to work in parallel with Jerusalem (J1), a data entry program was developed using the same technology and using the same database used for PC-tablet devices.

Data Cleaning After the completion of data entry and audit phase, data is cleaned by conducting internal tests for the outlier answers and comprehensive audit rules through using SPSS program to extract and modify errors and discrepancies to prepare clean and accurate data ready for tabulation and publishing.

Response rate

The response rate reached 83.7%.

Sampling error estimates

Sampling Errors Data of this survey affected by sampling errors due to use of the sample and not a complete enumeration. Therefore, certain differences are expected in comparison with the real values obtained through censuses. Variance were calculated for the most important indicators, there is no problem to disseminate results at the national level and at the level of the West Bank and Gaza Strip.

Non-Sampling Errors Non-Sampling errors are possible at all stages of the project, during data collection or processing. These are referred to non-response errors, response errors, interviewing errors and data entry errors. To avoid errors and reduce their effects, strenuous efforts were made to train the field workers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, as well as practical and theoretical training during the training course.

The implementation of the survey encountered non-response where the case (household was not present at home) during the fieldwork visit become the high percentage of the non-response cases. The total non-response rate reached 16.3%.