The current dataset represents data collection Online Community Based Ecological Monitoring in the Bering Sea. The online BeringWatch database is a system developed to facilitate community based ecological monitoring efforts in Bering Sea villages. It is an online database tool for non-scientists in remote locations to record and communicate environmental and ecological events. The system utilizes a database structure developed and refined over the past 10 years by the Tanam Amgig^naan (Island Sentinel) Programs of St. Paul and St. George Islands, Alaska. The online system has been successfully operating for several years (initial implementation phase) in a small selection of remote Aleutian Island and Bering Sea communities.The system was designed to be flexible and expandable in order to accommodate the diverse needs of multiple communities and the potential for more detailed data formats and languages. Recent upgrades to the system include quality control mechanisms that are integrated directly into the system, data collection protocols, and a formalized external expert review panel. The refinement of the system is also intended to facilitate usage by a broader user group within each community through the creation of a “Citizen Sentinel” program. In order to accomplish this we have refined our data output mechanism to allow maximal exposure of data collection efforts, especially for the purpose of community education and interaction with external scientists. An integral component of this effort is the consolidation and conversion of data collected by the the Aleut Community of St. Paul-Tribal Government-Ecosystem Conservation Office (ECO) and the St. George Traditional Council Kayumixtax Eco-Office from 2000 to 2011 into the new online database format. These datasets were archived as part of the North Pacific Research Board legacy project recovery effort undertaken by Axiom Data Science and NPRB in 2025. The goal of the recovery effort was to assess the NPRB-funded data projects from 2002 to 2014 and archive final data packages that were ready for publication to increase long-term accessibility and discoverability. Data packages were archived as is given limited funding and resources.

The oldest societal issue that has ever been is poverty, which is also the hardest to overcome. It is both unmeasurable and multidimensional. Decomposing rural multidimensional poverty is therefore a crucial method of measurement. The majority of poverty studies are typically designed for macroeconomic considerations, are general, and are subject to significant sampling errors. Thus, measuring poverty for a specific locality with various configurations is crucial for economic development. A processed and analyzed dataset from Goa, Camarines Sur's extensive community-based monitoring system is presented in this work. The local is situated in the poorest district, of the poorest province, in the poorest region of Luzon, Philippines. Research about poverty in this area is limited and measuring poverty at specific locality is scarce. The datasets contain the multidimensional poverty indicators, health, and nutrition, housing and settlement, water and sanitation, basic education from elementary to senior high school, income classifications, employment and livelihood, peace and order, summary of calamity occurrences experienced by residents, disaster risk reduction preparedness, figures of diagnostic analytics, tables of descriptive analytics, poverty analytics, measurement of decomposed poverty, summary of disaggregated configurations, graphs of predictive and prescriptive analytics, and population dynamics. This work is vital in analyzing poverty in rural and multidimensional approaches through poverty incidence, poverty gap, severity statistics, watts index, and classifications. It may also serve as a basis for measuring poverty from nearby regions and nations that use complete enumeration of its households and members. By utilizing the analyzed and processed data, further classifications and regressions can be done. It can be freely used by the government, private organizations, charitable institutions, businesses, academia, and researchers to target policies. An advantage of utilizing the dataset is to address multifaceted poverty that requires different interventions. It will facilitate the creation of programs to alleviate poverty and promote local economic development.

Increasing awareness of the issue of deforestation and degradation in the tropics has resulted in efforts to monitor forest resources in tropical countries. Advances in satellite-based remote sensing and ground-based technologies have allowed for monitoring of forests with high spatial, temporal and thematic detail. Despite these advances, there is a need to engage communities in monitoring activities and include these stakeholders in national forest monitoring systems. In this study, we analyzed activity data (deforestation and forest degradation) collected by local forest experts over a 3-year period in an Afro-montane forest area in southwestern Ethiopia and corresponding Landsat Time Series (LTS). Local expert data included forest change attributes, geo-location and photo evidence recorded using mobile phones with integrated GPS and photo capabilities. We also assembled LTS using all available data from all spectral bands and a suite of additional indices and temporal metrics based on time series trajectory analysis. We predicted deforestation, degradation or stable forests using random forest models trained with data from local experts and LTS spectral-temporal metrics as model covariates. Resulting models predicted deforestation and degradation with an out of bag (OOB) error estimate of 29% overall, and 26% and 31% for the deforestation and degradation classes, respectively. By dividing the local expert data into training and operational phases corresponding to local monitoring activities, we found that forest change models improved as more local expert data were used. Finally, we produced maps of deforestation and degradation using the most important spectral bands. The results in this study represent some of the first to combine local expert based forest change data and dense LTS, demonstrating the complementary value of both continuous data streams. Our results underpin the utility of both datasets and provide a useful foundation for integrated forest monitoring systems relying on data streams from diverse sources.

BackgroundReducing neonatal and child mortality is a key component of the health-related sustainable development goal (SDG), but most low and middle income countries lack data to monitor child mortality on an annual basis. We tested a mortality monitoring system based on the continuous recording of pregnancies, births and deaths by trained community-based volunteers (CBV).Methods and findingsThis project was implemented in 96 clusters located in three districts of the Northern Region of Ghana. Community-based volunteers (CBVs) were selected from these clusters and were trained in recording all pregnancies, births, and deaths among children under 5 in their catchment areas. Data collection lasted from January 2012 through September 2013. All CBVs transmitted tallies of recorded births and deaths to the Ghana Birth and deaths registry each month, except in one of the study districts (approximately 80% reporting). Some events were reported only several months after they had occurred. We assessed the completeness and accuracy of CBV data by comparing them to retrospective full pregnancy histories (FPH) collected during a census of the same clusters conducted in October-December 2013. We conducted all analyses separately by district, as well as for the combined sample of all districts. During the 21-month implementation period, the CBVs reported a total of 2,819 births and 137 under-five deaths. Among the latter, there were 84 infant deaths (55 neonatal deaths and 29 post-neonatal deaths). Comparison of the CBV data with FPH data suggested that CBVs significantly under-estimated child mortality: the estimated under-5 mortality rate according to CBV data was only 2/3 of the rate estimated from FPH data (95% Confidence Interval for the ratio of the two rates = 51.7 to 81.4). The discrepancies between the CBV and FPH estimates of infant and neonatal mortality were more limited, but varied significantly across districts.ConclusionsIn northern Ghana, a community-based data collection systems relying on volunteers did not yield accurate estimates of child mortality rates. Additional implementation research is needed to improve the timeliness, completeness and accuracy of such systems. Enhancing pregnancy monitoring, in particular, may be an essential step to improve the measurement of neonatal mortality.

BackgroundCentral American countries face a major challenge in the control of Triatoma dimidiata, a widespread vector of Chagas disease that cannot be eliminated. The key to maintaining the risk of transmission of Trypanosoma cruzi at lowest levels is to sustain surveillance throughout endemic areas. Guatemala, El Salvador, and Honduras integrated community-based vector surveillance into local health systems. Community participation was effective in detection of the vector, but some health services had difficulty sustaining their response to reports of vectors from the population. To date, no research has investigated how best to maintain and reinforce health service responsiveness, especially in resource-limited settings.Methodology/Principal FindingsWe reviewed surveillance and response records of 12 health centers in Guatemala, El Salvador, and Honduras from 2008 to 2012 and analyzed the data in relation to the volume of reports of vector infestation, local geography, demography, human resources, managerial approach, and results of interviews with health workers. Health service responsiveness was defined as the percentage of households that reported vector infestation for which the local health service provided indoor residual spraying of insecticide or educational advice. Eight potential determinants of responsiveness were evaluated by linear and mixed-effects multi-linear regression. Health service responsiveness (overall 77.4%) was significantly associated with quarterly monitoring by departmental health offices. Other potential determinants of responsiveness were not found to be significant, partly because of short- and long-term strategies, such as temporary adjustments in manpower and redistribution of tasks among local participants in the effort.Conclusions/SignificanceConsistent monitoring within the local health system contributes to sustainability of health service responsiveness in community-based vector surveillance of Chagas disease. Even with limited resources, countries can improve health service responsiveness with thoughtful strategies and management practices in the local health systems.

The elderly safety monitoring equipment market, currently valued at $715 million in 2025, is projected to experience robust growth, driven by several key factors. An aging global population, increasing incidences of chronic diseases and disabilities among the elderly, and a rising demand for independent living solutions are major contributors to this market expansion. Technological advancements, such as the integration of AI and IoT in wearable sensors and home monitoring systems, are further fueling market growth. The market is witnessing a shift towards sophisticated, integrated systems offering remote health monitoring, fall detection, and emergency response capabilities, exceeding the basic functionalities of traditional medical alert systems. Companies like GetSafe, One Call Alert, and Medical Guardian are leading this innovation, offering a range of products tailored to individual needs and preferences. While regulatory hurdles and concerns regarding data privacy may pose challenges, the overall market outlook remains positive, with a projected Compound Annual Growth Rate (CAGR) of 6% from 2025 to 2033. This growth will be significantly influenced by the adoption of advanced technologies and government initiatives promoting elderly care and independent living. The market segmentation, while not explicitly detailed, likely includes various product types (wearable sensors, home monitoring systems, GPS trackers), service models (subscription-based, one-time purchase), and deployment scenarios (in-home, community-based). Geographical variations in healthcare infrastructure and adoption rates will influence regional market growth. North America and Europe are expected to dominate the market initially, driven by higher disposable incomes and advanced healthcare systems. However, emerging markets in Asia-Pacific and Latin America are anticipated to witness substantial growth in the coming years as awareness of elderly safety and technological penetration increase. Competitive rivalry is expected to remain intense, with established players facing pressure from smaller, agile companies offering innovative solutions and competitive pricing. The market’s future success hinges on continued innovation, cost-effectiveness, and addressing the specific needs and preferences of the aging population.

Smart Probation Monitoring Platforms Market Outlook

According to our latest research, the Global Smart Probation Monitoring Platforms market size was valued at $1.8 billion in 2024 and is projected to reach $5.6 billion by 2033, expanding at a CAGR of 13.2% during the forecast period of 2025–2033. The primary driver behind this robust growth is the increasing adoption of digital transformation initiatives within government and law enforcement agencies, aimed at modernizing and streamlining probation monitoring processes. The integration of advanced technologies such as artificial intelligence, IoT-enabled devices, and real-time data analytics is revolutionizing the way probation and parolees are supervised, making monitoring more effective, reducing recidivism, and optimizing resource allocation. This technological shift is not only enhancing public safety but also reducing administrative burdens and operational costs for correctional institutions worldwide.

Regional Outlook

North America currently holds the largest share of the global Smart Probation Monitoring Platforms market, accounting for nearly 42% of the total market value in 2024. This dominance is attributed to the region's mature criminal justice infrastructure, early adoption of digital monitoring solutions, and robust government funding for public safety technology. The United States, in particular, has been at the forefront, with state and federal agencies investing heavily in electronic monitoring systems and cloud-based probation management platforms. Stringent regulatory frameworks, a high volume of probation and parole cases, and a strong ecosystem of technology vendors further solidify North America’s leadership. The region’s focus on reducing prison overcrowding and supporting community-based corrections has accelerated the deployment of innovative monitoring solutions, making it a benchmark for other regions.

Asia Pacific is projected to be the fastest-growing region, with a forecasted CAGR of 16.7% from 2025 to 2033. Rapid urbanization, increasing crime rates, and government initiatives to reform the justice system are fueling investments in smart probation monitoring technologies across countries such as China, India, Japan, and Australia. The region is witnessing significant interest from both local and international technology providers, who are partnering with government bodies to pilot and scale digital supervision programs. The rising adoption of cloud-based platforms and mobile monitoring applications, combined with improvements in digital infrastructure, is enabling more efficient probation management and expanding access to remote and underserved areas. This dynamic environment is expected to drive substantial market growth and position Asia Pacific as a key innovation hub in the coming years.

In contrast, emerging economies in Latin America, the Middle East, and Africa are experiencing a gradual but steady adoption of smart probation monitoring platforms. While these regions face challenges such as limited funding, insufficient digital infrastructure, and complex regulatory landscapes, there is growing recognition of the benefits of digital supervision in alleviating prison overcrowding and improving public safety. International aid programs, public-private partnerships, and pilot projects are helping to bridge the technology gap, but widespread implementation remains constrained by budgetary limitations and the need for localized solutions tailored to specific legal and cultural contexts. Nevertheless, as digital literacy improves and policy reforms gain momentum, these emerging markets present significant long-term growth opportunities for technology vendors and service providers.

Report Scope

Environmental Monitoring Market Outlook

According to our latest research, the global environmental monitoring market size reached USD 23.2 billion in 2024, registering a robust growth trajectory. The market is projected to expand at a CAGR of 8.7% from 2025 to 2033, reaching an estimated USD 49.1 billion by 2033. This sustained growth is primarily driven by increasing regulatory requirements, heightened public awareness about environmental health, and the integration of advanced technologies such as IoT and AI in environmental monitoring solutions.

One of the most significant growth factors propelling the environmental monitoring market is the tightening of global regulations regarding air, water, and soil quality. Governments worldwide are enforcing stricter emission and pollution standards, compelling industries and municipalities to invest in advanced monitoring systems. The proliferation of international agreements, such as the Paris Agreement, and national policies targeting climate change mitigation have further accelerated the adoption of environmental monitoring solutions. These regulatory frameworks not only mandate compliance but also encourage proactive monitoring, fostering a culture of environmental responsibility across sectors. Additionally, penalties for non-compliance and incentives for sustainable practices are boosting the demand for reliable and accurate monitoring technologies.

Another crucial driver is the rapid advancement and adoption of digital technologies in environmental monitoring. The integration of IoT, big data analytics, cloud computing, and artificial intelligence has revolutionized the way environmental data is collected, analyzed, and reported. These technologies enable real-time monitoring, predictive analytics, and automated reporting, significantly improving the efficiency and effectiveness of environmental management. The deployment of smart sensors and wireless networks has made it possible to monitor multiple environmental parameters in real-time, even in remote and inaccessible locations. This technological evolution is not only reducing operational costs but also enhancing the accuracy and timeliness of environmental data, which is critical for informed decision-making and swift response to environmental incidents.

Public awareness and activism around environmental issues have reached unprecedented levels, influencing both policy and corporate behavior. The increasing frequency and severity of environmental disasters, such as wildfires, floods, and industrial accidents, have heightened public concern and demand for transparency. Social media and global news coverage have amplified these concerns, pressuring governments and corporations to adopt comprehensive environmental monitoring systems. Furthermore, the rise of citizen science initiatives and community-based monitoring programs is democratizing environmental data collection, making it more accessible and actionable. This groundswell of public interest is expected to sustain the market's growth momentum in the coming years, as stakeholders recognize the value of reliable environmental information in safeguarding public health and ecological integrity.

From a regional perspective, North America and Europe continue to lead the environmental monitoring market, driven by stringent regulations, high levels of industrialization, and strong governmental support for environmental initiatives. However, the Asia Pacific region is emerging as a significant growth engine, fueled by rapid urbanization, industrial expansion, and increasing environmental awareness among policymakers and the public. Countries such as China, India, and Japan are investing heavily in environmental infrastructure and technology, presenting lucrative opportunities for market players. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, with growing investments in pollution control and sustainable development projects. This diverse regional landscape underscores the global relevance and necessity of environmental monitoring solutions.

The Electronic Offender Monitoring (EOM) market is experiencing robust growth, driven by increasing crime rates, a rising preference for cost-effective alternatives to incarceration, and advancements in monitoring technologies. The market's expansion is further fueled by the growing adoption of GPS tracking devices, smartphone-based monitoring solutions, and the integration of AI and machine learning for enhanced surveillance and risk assessment. Government initiatives promoting community corrections and rehabilitation programs are also significantly contributing to the market's upward trajectory. While challenges remain, such as concerns about privacy violations and the potential for technological failures, the overall market outlook remains positive. We estimate the 2025 market size to be around $2.5 billion, based on industry reports indicating a consistent growth rate within the correctional technology sector. This figure is projected to increase steadily over the forecast period (2025-2033), driven by the factors mentioned above. The competitive landscape is characterized by a mix of established players and emerging technology providers. Major companies like Alcohol Monitoring Systems, G4S, and Securus Technologies are leveraging their extensive experience in security and surveillance to dominate market share. However, innovative startups and smaller firms are rapidly gaining ground by offering advanced solutions that integrate AI, IoT, and cloud-based analytics. This increased competition is driving innovation and lowering costs, making EOM solutions more accessible to correctional facilities and law enforcement agencies. Regional variations exist, with North America and Europe currently leading the market, followed by Asia-Pacific. However, emerging economies are expected to witness significant growth in the coming years due to increasing investments in public safety infrastructure. The long-term forecast for the EOM market is optimistic, with consistent growth expected throughout the next decade.

MaMoni Health Systems Strengthening (MaMoni HSS) is a USAID-funded project of four years duration (2013-2017) under the global Maternal and Child Health Integrated Program (MCHIP) – an associate award, which focuses on strengthening the systems and standards for maternal, newborn and child health, family planning and nutrition (MNCH/FP/N). Through funding from USAID in 2013, MaMoni HSS was started under MCHIP and is implemented by a consortium led by Jhpiego in partnership with Save the Children, John Snow Inc. (JSI), and Johns Hopkins University Institute of International Programs (JHU/IIP). In Bangladesh, the implementation is led by Save the Children in close partnership with the Government of Bangladesh and other national partners and collaborating agencies. Since its inception, project monitoring data were collected through community-based surveys every six months to track the progress of implementation; this process was labeled “tracer survey” since data were collected on only a few indicators in order to monitor population-level coverage of the project. Questions covered maternal health, newborn health, and family planning interventions from recently delivered women (RDW) and from currently married women of reproductive age (CMWRA, 15-49 years). These data were used locally to guide project implementation and were not disseminated outside the project.

The global electronic monitoring bracelets market is experiencing robust growth, driven by increasing crime rates, the need for cost-effective alternatives to incarceration, and advancements in technology enabling more precise and reliable monitoring. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several key factors: the rising adoption of GPS tracking technology for improved location accuracy and real-time monitoring, the increasing demand for community-based corrections programs that leverage electronic monitoring, and the integration of biometric sensors within bracelets to verify the identity of the wearer and detect tampering attempts. Furthermore, the expanding applications beyond criminal justice, including patient monitoring in healthcare and employee tracking in various industries, contribute to market expansion. However, certain challenges restrain market growth. Concerns regarding data privacy and security remain significant, especially given the sensitive nature of the data collected. The high initial investment costs for implementing and maintaining electronic monitoring systems can also deter adoption, particularly in resource-constrained regions. Regulatory hurdles and differing legal frameworks across jurisdictions further complicate market penetration. Despite these restraints, the overall trend points towards sustained growth, driven by technological innovation and the increasing recognition of electronic monitoring as a valuable tool for managing risk and enhancing public safety. The market is segmented by technology type (GPS, RFID, etc.), application (criminal justice, healthcare, etc.), and geography, with North America and Europe currently holding significant market share due to advanced infrastructure and established legal frameworks. Key players like Laipac, Allied Universal, Supercom, and others are actively innovating and expanding their product portfolios to capitalize on market opportunities.

Curfew Monitoring and Reporting Market Outlook

As per our latest research, the global Curfew Monitoring and Reporting market size in 2024 stands at USD 1.84 billion, demonstrating robust momentum driven by technological advancements and the increasing need for effective offender management. The market is experiencing a healthy growth trajectory with a CAGR of 9.2% from 2025 to 2033. By 2033, the market is forecasted to reach USD 4.10 billion, reflecting the growing adoption of digital monitoring solutions across multiple sectors. The primary growth factor is the rising emphasis on public safety, compliance mandates, and the integration of advanced tracking technologies.

The expansion of the Curfew Monitoring and Reporting market is significantly influenced by the global surge in criminal justice reforms, which prioritize alternative sentencing and community-based corrections. Governments and judicial systems are increasingly turning to electronic monitoring solutions to manage offenders outside traditional incarceration settings. This shift is not only cost-effective but also helps reduce overcrowding in correctional facilities. The integration of real-time tracking, automated reporting, and data analytics further enhances the efficiency and reliability of curfew monitoring systems, making them indispensable tools for law enforcement agencies and judiciary bodies worldwide.

Another pivotal growth driver is the rapid technological evolution within the market. The adoption of GPS-based and biometric technologies has revolutionized curfew monitoring by offering precise location tracking and secure identity verification. These advancements have improved compliance rates and reduced the risk of tampering or circumvention. Additionally, the proliferation of cloud-based platforms has enabled remote access to monitoring data, facilitating seamless information sharing among stakeholders. The continuous development of user-friendly interfaces and mobile applications has also contributed to higher adoption rates among end-users, including private security firms and government agencies.

The increasing prevalence of domestic violence and juvenile offenses has further fueled demand for curfew monitoring solutions tailored to these sensitive applications. Courts and social services agencies are leveraging these systems to ensure compliance with restraining orders and curfews, thereby enhancing victim safety and offender accountability. Moreover, the growing trend of privatization in the security sector has opened new avenues for private security firms to offer specialized monitoring services. These dynamics, coupled with favorable regulatory frameworks and funding initiatives, are expected to sustain market growth over the forecast period.

Regionally, North America continues to dominate the Curfew Monitoring and Reporting market due to its advanced legal infrastructure, high technology adoption, and supportive government policies. Europe follows closely, driven by progressive criminal justice reforms and increased investments in public safety technologies. The Asia Pacific region is poised for the fastest growth, propelled by rising urbanization, growing crime rates, and government initiatives to modernize law enforcement. Latin America and the Middle East & Africa are also witnessing gradual adoption, primarily through pilot projects and international collaborations aimed at enhancing public safety and judicial efficiency.

Component Analysis

The Component segment of the Curfew Monitoring and Reporting market comprises hardware, software, and services, each playing a critical role in the overall ecosystem. Hardware components, such as electronic monitoring devices, ankle bracelets, and sensors, form the backbone of curfew compliance systems. These devices are designed to be tamper-resistant and capable of withstanding various environmental conditions, ensuring continuous and reliable operation. Advances in minia

The data set covers a 101-year period (1915-2016) of quadrat-based plant sampling at the Jornada Experimental Range in southern New Mexico. At each sampling event, a pantograph was used to record the location and perimeter of living plants within permanent quadrats. Basal area was recorded for perennial grass species, canopy cover area was recorded for shrub species, and all other perennial species were recorded as point data. The data set includes 122 1m by 1m permanent quadrats, although not all quadrats were sampled in each year of the study and there is a gap in monitoring from 1980-1995. These data provide a unique opportunity to investigate changes in the plant community over 100 years of variation in precipitation and other environmental conditions. We provide the following data and data formats: (1) the digitized maps in shapefile format; (2) data table containing coordinates (x,y) of perennial species within quadrats, including cover area for grasses and shrubs; (3) data table of counts of annual plant individuals per quadrat; (4) species list indicating growth form and habit of recorded species; (5) table of dates when each quadrat was sampled; (6) table of the pasture each quadrat was located within (note that pasture boundaries have changed over time). Additional data to help characterize plant-scale factors related to vegetation dynamics at the quadrat locations are: (7) data table of depth to caliche layer; (8) data table of soil particle size analysis and sand fractionation; and (9) data table of local and patch topography. This data package was created to support a specific data paper. Data are also available in data packages knb-lter-jrn.210351001, knb-lter-jrn.210351002, and knb-lter-jrn.210351003. Pantograph sampling is currently conducted at 5 year intervals by USDA-ARS staff, and new data will be added to those data packages periodically.

Odor Monitoring System Market Outlook

According to our latest research, the global Odor Monitoring System market size reached USD 3.2 billion in 2024, reflecting a robust demand for advanced environmental monitoring solutions worldwide. The market is expected to witness a strong compound annual growth rate (CAGR) of 9.6% from 2025 to 2033, driven by stringent environmental regulations, increasing industrialization, and rising public awareness of air quality. By 2033, the global odor monitoring system market is forecasted to reach USD 7.3 billion, as per our detailed analysis and projections. This impressive growth trajectory is underpinned by the integration of advanced sensor technologies, real-time monitoring capabilities, and the expanding application scope across multiple industries.

The primary growth factor for the odor monitoring system market is the increasing enforcement of environmental regulations by governments and regulatory bodies worldwide. As urbanization and industrialization accelerate, concerns regarding air pollution and odor emissions from factories, wastewater treatment plants, and landfill sites have become more pronounced. Regulatory agencies are mandating continuous and accurate monitoring of air quality, including odor emissions, to ensure compliance and protect public health. This has compelled industries to invest in state-of-the-art odor monitoring systems that provide real-time data, early detection of hazardous emissions, and automated reporting. The deployment of these systems not only helps organizations avoid penalties but also enhances their corporate social responsibility profiles, further stimulating market growth.

Another significant driver is the rapid technological advancement in sensor technologies and data analytics. The adoption of Internet of Things (IoT) platforms, artificial intelligence (AI), and cloud-based data management is revolutionizing the odor monitoring system market. Modern odor monitoring systems are equipped with highly sensitive chemical, biological, and gas sensors that can detect a wide range of odorants at very low concentrations. These systems are increasingly integrated with wireless connectivity and cloud infrastructure, enabling remote monitoring, predictive maintenance, and comprehensive data analytics. Such technological innovations are making odor monitoring systems more accurate, user-friendly, and cost-effective, thereby broadening their adoption across diverse sectors such as industrial manufacturing, food and beverage, healthcare, and residential applications.

Furthermore, increasing public awareness and community activism regarding environmental quality are propelling the demand for odor monitoring systems. Communities located near industrial facilities, landfills, and wastewater treatment plants are increasingly voicing concerns over unpleasant odors and potential health risks. This has led to a growing trend of public-private partnerships, where municipalities and industries collaborate to implement advanced odor monitoring solutions. These initiatives not only help in mitigating odor-related complaints but also foster transparency and trust between industries and local communities. As a result, the market is witnessing a surge in demand from both public and private sectors, creating new opportunities for market players to innovate and expand their offerings.

The introduction of specialized devices like the Bathroom Odor Sensor is a testament to the expanding application of odor monitoring technologies in everyday environments. These sensors are designed to detect and neutralize unpleasant odors in residential and commercial bathrooms, ensuring a pleasant and hygienic atmosphere. By integrating advanced sensor technology with smart home systems, these devices offer real-time monitoring and control, allowing users to maintain optimal air quality effortlessly. The growing demand for such targeted solutions highlights the increasing consumer awareness and desire for enhanced indoor air quality. As more households and businesses prioritize health and comfort, the market for bathroom odor sensors is expected to see significant growth, further diversifying the landscape of odor monitoring applications.

From a regional perspective, Asia Pacific is emerging as the fastest-growing market for odor monitoring systems, driven by

The Electronic Offender Monitoring Equipment (EOME) market is experiencing robust growth, driven by increasing crime rates globally and the rising adoption of community-based corrections programs as a cost-effective alternative to incarceration. Technological advancements, such as the development of more sophisticated GPS tracking devices and improved data analytics capabilities, are further fueling market expansion. The market is segmented by application (GPS tracking, inmate monitoring, home curfew, domestic violence deterrence) and type (ankle monitor, electronic bracelet, other hardware), with GPS tracking and ankle monitors currently dominating the market share. However, the demand for more comfortable and less intrusive devices, like electronic bracelets, is expected to increase significantly in the coming years. This shift is likely to drive innovation in device miniaturization and the integration of biometric sensors for enhanced monitoring capabilities. The North American market, particularly the United States, currently holds a significant share due to established correctional infrastructure and substantial government funding for offender monitoring programs. However, other regions, including Europe and Asia-Pacific, are witnessing a surge in adoption rates, driven by increasing crime rates and growing awareness of the benefits of EOME. Restraints to market growth include concerns about privacy violations, the potential for equipment malfunction, and the high initial investment costs associated with implementing comprehensive monitoring systems. Despite these challenges, the long-term outlook for the EOME market remains positive, with significant growth opportunities projected throughout the forecast period. The ongoing development of advanced features like AI-powered alerts for potential violations and integration with other criminal justice systems will further enhance the efficiency and effectiveness of EOME. This will lead to increased adoption across various jurisdictions and contribute to a more robust and sophisticated monitoring system. The competitive landscape is characterized by a mix of established players and emerging technology companies. Companies are increasingly focusing on providing comprehensive solutions that integrate hardware, software, and data analytics capabilities to offer clients complete monitoring and management platforms. Strategic partnerships and acquisitions are likely to be key strategies for market players to expand their reach and gain a competitive edge. Continued regulatory scrutiny and the need to address privacy concerns remain significant factors that companies must navigate effectively to ensure sustainable growth. The focus on cost-effectiveness and improved accuracy of monitoring systems will likely drive innovation and expansion in the years to come.

Coral Reef Monitoring AI Market Outlook

As per our latest research, the global Coral Reef Monitoring AI market size reached USD 245.8 million in 2024, demonstrating remarkable momentum driven by technological advancements and growing environmental concerns. The market is set to expand at a robust CAGR of 18.7% from 2025 to 2033, with the forecasted market size projected to reach USD 1,123.6 million by 2033. The primary growth factor fueling this surge is the increasing adoption of artificial intelligence for real-time, scalable, and cost-effective coral reef monitoring solutions, addressing urgent needs for marine ecosystem conservation and sustainable management.

The accelerating degradation of coral reefs due to climate change, ocean acidification, pollution, and overfishing has underscored the necessity for advanced monitoring tools. Traditional reef monitoring methods are often labor-intensive, time-consuming, and limited in spatial and temporal coverage. In contrast, the integration of AI technologies—such as machine learning, computer vision, and deep learning—has revolutionized the monitoring landscape by enabling automated, high-resolution, and continuous data analysis. This technological leap not only enhances the accuracy of coral health assessments and bleaching detection but also empowers stakeholders to implement timely interventions, thereby driving the widespread adoption of AI-powered solutions in the coral reef monitoring market.

Another significant growth driver is the increasing collaboration between research institutes, government agencies, and environmental organizations. These collaborations have resulted in the pooling of resources, data sharing, and the development of open-source AI models tailored for marine environments. Such partnerships have accelerated innovation and reduced the entry barriers for deploying AI-driven monitoring systems, especially in developing regions where coral reefs are most vulnerable. Moreover, the global push for sustainable development and the United NationsÂ’ Sustainable Development Goals (SDGs) have further catalyzed funding and policy support, fostering a conducive ecosystem for the expansion of the coral reef monitoring AI market.

The proliferation of remote sensing technologies, underwater drones, and IoT-enabled sensors has also played a pivotal role in market growth. The synergy between these hardware advancements and sophisticated AI algorithms has facilitated the collection and analysis of vast datasets, spanning diverse reef habitats across the globe. This has enabled granular monitoring of reef health, species diversity, and water quality parameters, providing actionable insights for conservationists and policymakers. As AI solutions become more accessible and affordable, their adoption is expected to permeate a wider spectrum of end-users, from large-scale government initiatives to localized community-based monitoring programs, further propelling the marketÂ’s upward trajectory.

The advent of Autonomous Coral Reef Mapping Robots represents a significant breakthrough in the field of marine conservation. These sophisticated robots are designed to autonomously navigate underwater environments, capturing high-resolution images and data of coral reef structures. By utilizing advanced sensors and AI-driven algorithms, these robots can map extensive reef areas with precision and efficiency, offering insights into coral health, biodiversity, and habitat conditions. The deployment of such autonomous systems reduces the need for human divers, minimizing the risks associated with underwater exploration and allowing for continuous monitoring in challenging conditions. As these robots become more integrated into coral reef monitoring programs, they are expected to enhance the accuracy and timeliness of data collection, supporting more informed conservation strategies and policy-making.

Regionally, the Asia Pacific region stands out as the largest and fastest-growing market for coral reef monitoring AI, owing to its extensive coral reef systems and heightened vulnerability to environmental threats. North America and Europe follow closely, driven by strong research infrastructure and proactive governmental support. In contrast, Latin America and the Middle East & Africa are emerging as promising markets, buoyed by increasing awareness and international funding for marine conservation. T

This repository contains raw datasets gathered under the EPSRC-funded Humanitarian Engineering and Energy for Displacement (HEED) research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities by creating an evidence base on the usage of seven different energy interventions, and provide recommendations for improved design of future energy interventions to better meet the needs of people. Below is a brief description of the interventions.

1. Stove-use monitoring systems (July 2019 to October 2019) - Stove-use monitoring systems (SUMs) were deployed on clay stoves in Kigeme camp, Rwanda in July 2019. The aim of the study was to evaluate stove usage patterns by measuring temperature profiles within stove enclosure and on the surface of stoves. The SUMs consisted of 2 sensors - a thermocouple to measure temperature within the stove and a Si7021 sensor to measure temperature outside the stove, connected to an Arduino MKR GSM 1400 board. The data measured by the sensors was stored only if the change in values exceeded a set threshold for either of the readings. The SUMs were powered by a re-chargeable Li-Ion battery of 3.7V and a rating of 7.59Wh.
   
   The study was conducted in 2 phases. In phase 1 (02 July 2019 to 30 September 2019), data was collected from 15 SUMs and stored locally on SD cards as well as communicated to a remote server via GSM. The time of data collection was recorded using GSM functionality. However, several GSM and MQTT failures were noted leading to loss of timestamp values as well as shorter battery lifetime due to re-transmission tries. In phase 2 (02 October 2019 to 17 October 2019), data was collected from 9 SUMs and only stored locally on SD cards. The time of data collection was recorded using an external RTC clock connected to the Arduino board. The data from both phases of study is deposited here in SUM.zip. The cleaned dataset is available at 10.5281/zenodo.3946999.
   
2. Mobile Lantern monitoring systems (July 2019 to December 2019) - Mobile lantern monitoring systems (LMSs) were deployed in Nyabiheke camp, Rwanda, in July 2019. The aim of the study was to evaluate lantern usage (static or mobile) and consumption (charge and discharge) patterns. The monitors consisted of a D.light S30 solar lantern fitted with an Arduino-based monitoring device. The most integral part of the device was the Arduino MKR GSM 1400 board connected to an ADXL345 inertial motion unit sensor. The ADXL was used to calculate step count of a user based on activity and freefall interrupts. Additionally, the voltage of lantern battery was measured using an in-house voltage monitor to understand the discharging and charging patterns. The step count and battery voltage data were stored only if a significant change in the step count was detected. The LMSs were powered through a re-chargeable Li-Ion battery of 3.7V and a rating of 7.59Wh.
   
   The study was conducted in 2 phases. In phase 1 (03 July 2019 to 30 September 2019), data was collected from 60 lanterns and stored locally on SD card as well as communicated to a remote server via GSM. The time of data collection was recorded using GSM functionality. However, several GSM and MQTT failures were noted leading to loss of timestamp values as well as shorter battery lifetime due to re-transmission tries. In phase 2 (09 October 2019 to 18 December 2019), the design of lantern monitors was modified to circumvent these issues. The data was collected from 54 lanterns data and only stored locally on SD cards. The time of data collection was recorded using an external RTC clock connected to the Arduino board. Additionally, an internal watchdog timer was used to reset the device in case of failures. While certain failures persisted, the data yield was considerably higher than phase 1 of the study. The data from both phases of study is deposited here in LMS.zip. The cleaned dataset is available at 10.5281/zenodo.4269809.
   
3. Individual appliance monitors (December 2018 to January 2020) - Individual appliance monitors (IAMs) were deployed in Uttargaya settlement, Nepal, in December 2018. The IAMs were simple, cost-effective and unobtrusive devices to collect data on the energy usage of connected appliances. The aim of the study was to understand energy consumption and usage patterns of different household appliances in grid-connected sub-metered displaced communities. The monitoring system consisted of 2 types of devices – Energenie MiHome Smart Plugs MIHO005 (referred to as the IAM) to sense data relating to power and voltage drawn by the connected appliance, and gateway nodes to collect data from IAM. The main component of the gateway node was a Raspberry Pi fitted with an Energenie ENER314-RT (receiver-transmitter) add-on board to allow the Pi to communicate with the smart plugs. The data collected by the RPi gateway was stored locally in an SD card as well as sent to a remote server hosted at Coventry University.
   
   The study was conducted until January 2020. The raw data from the study is deposited here in IAM.zip. The cleaned dataset is available at 10.5281/zenodo.4271714.
   
4. Footfall monitoring systems (December 2018 to January 2020) - Seven footfall monitoring systems (FMSs) were deployed alongside seven solar streetlights to measure step count of passers-by in the Uttargaya settlement, Nepal, in December 2018. The aim of the study was to understand the level of pedestrian movement in the area and evaluate the effect of streetlights on the level of activity. Therefore, the footfall monitors were deployed prior to commissioning of streetlights to gather baseline data. The footfall monitors consisted of a Raspberry Pi 3B, PiFace Real Time Clock and CAM008 70º night vision IR sensor to detect footfall. Upon detection, footfall count along with the direction of movement and the timestamp (measured from PiFace RTC) was stored onto an SD card and communicated to a remote server hosted at Coventry University.
   
   The study was conducted until January 2020. The raw data from the study is deposited here in FMS.zip. The cleaned dataset is available at 10.5281/zenodo.4271730.
   
5. Standalone Solar System for a Community Hall (June 2019 to March 2021) - A standalone solar system was deployed in a Community Hall in Nyabiheke camp, Rwanda in June 2019. The aim of the study was to understand the energy consumption behavior within a set location, and create an evidence base on the value of energy and its benefits for growing cooperatives and learning communities. The standalone system comprised of 2kW of solar panels and 12.2 kWh GEL battery storage capacity. Additional components included a Victron 150/35 charge controller and a Venus GX and 48/3000 MultiPlus Inverter. The system powered four AC 2-pin sockets, a 30 W entrance light, and six 30 W indoor lights. Each light and socket were individually metered and controlled via a remote monitoring unit. This allowed for quotas, maximum draws and periods of use to be remotely controlled.
   
   The study was conducted until March 2021. The raw data from the study is deposited here in Hall.zip. The cleaned dataset until March 2020 is available at 10.5281/zenodo.3949776.
   
6. PV-battery Microgrid (July 2019 to March 2021) - A PV-battery Microgrid was deployed in Kigeme camp, Rwanda in July 2019. The microgrid powered a playground and two nursery buildings. The aim of the study was to identify best practice in the construction, control and operation of a micro-grid as a shared resource, understand optimal design features for user interfaces that allow negotiation over energy priorities and needs and understand community priorities for energy in the context of early years education and the rate of growth in energy utilization. The micro-grid system comprised of a 2.5 kW of solar panels and 21.1 kWh GEL battery storage capacity. Additional components included a Victron 250/60 charge controller, Venus GX 48/1200 MultiPlus Inverter and BMV-700 series battery monitor. Each Nursery building had three classrooms (A, B and C) with separate entrances. Each classroom was fitted with an AC socket, five 10 Watt indoor lights and a 10 Watt outdoor entrance light. A spare socket was located in the first classroom of each nursery building (Classroom A). Two outdoor double sockets were installed at the playground, and fifteen 10 Watt lights were located in the roof structure. Three transmission line poles were fitted with three 10 Watt lights for safety and security purposes, which also enabled them to act as streetlights. Each light and socket was individually monitored and controlled via a programmable remote monitoring unit (RMU). Wireless AC smart meters were used to control and measure power consumption at the socket loads. These meters communicated with the RMU to receive commands and notified the RMU when a command had been received and to transmit usage data. Each light was connected to a CPE (customer-premises equipment) unit, with three lights per CPE, which communicated wirelessly with the RMU. The CPEs received information from the RMU on when to turn the lights on/off and set the brightness. The CPE also monitored the power consumption of the three lights.
   
   The study was conducted until March 2021. The raw data from the study is deposited here in Microgrid.zip. The cleaned dataset until March 2020 is available at 10.5281/zenodo.3949776.
   
7. Standalone solar streetlights (Nepal - June 2019 to October 2020; Rwanda - July 2019 to March 2021) - Seven

The Electronic Offender Monitoring (EOM) software market is experiencing robust growth, driven by increasing crime rates, the need for cost-effective alternatives to incarceration, and advancements in GPS and alcohol detection technologies. The market, estimated at $2 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $7 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising adoption of GPS tracking systems for various applications, including inmate monitoring, home curfew programs, and domestic violence deterrence initiatives, is significantly contributing to market growth. Secondly, technological advancements, such as the development of more accurate and reliable alcohol monitoring systems, are enhancing the effectiveness and reliability of EOM solutions. Furthermore, increasing government initiatives focused on rehabilitation and community-based corrections are fostering a favorable regulatory environment for the expansion of EOM technologies. However, the market faces certain restraints. Data privacy concerns and the potential for system malfunctions or inaccuracies remain significant challenges. The high initial investment costs associated with implementing and maintaining EOM systems can also hinder widespread adoption, particularly in resource-constrained regions. Despite these challenges, the overall market outlook remains positive, driven by the increasing preference for cost-effective and efficient crime management strategies. The segmentation of the market into application types (GPS tracking, inmate monitoring, home curfew, domestic violence deterrence) and device types (alcohol monitoring systems, GPS devices) presents various opportunities for specialized players. North America currently dominates the market share, followed by Europe and Asia Pacific, reflecting higher adoption rates in developed economies. Growth in developing nations is anticipated as awareness and infrastructure improves.

Fence Line Air Quality Monitoring Market Outlook

According to our latest research, the global fence line air quality monitoring market size reached USD 398.7 million in 2024, with a robust compound annual growth rate (CAGR) of 8.4% projected through the forecast period. By 2033, the market is anticipated to reach USD 819.6 million, driven by increasingly stringent environmental regulations, heightened awareness of industrial emissions, and the growing adoption of advanced monitoring technologies. The market’s expansion is underpinned by the urgent need for real-time, accurate data to ensure regulatory compliance and protect public health, particularly in regions with dense industrial activity and urbanization.

Growth in the fence line air quality monitoring market is primarily fueled by the tightening of environmental standards globally. Regulatory bodies such as the US Environmental Protection Agency (EPA) and the European Environment Agency (EEA) have enforced strict emission limits for industrial facilities, chemical plants, and power generation sites. These regulations mandate continuous and precise monitoring of pollutants at facility perimeters, compelling industries to invest in sophisticated monitoring systems. Furthermore, increasing public concern over air quality and its direct link to health issues such as respiratory diseases and cardiovascular problems has intensified the demand for reliable fence line air quality monitoring solutions. The integration of real-time data analytics and reporting capabilities in these systems further enhances their value proposition for both regulatory compliance and community engagement.

Technological advancements are playing a pivotal role in the rapid evolution of the fence line air quality monitoring market. Innovations in sensor technology, remote sensing, and data analytics have significantly improved the accuracy, sensitivity, and ease of deployment of monitoring systems. Modern sensor-based monitoring devices offer real-time detection of a wide range of pollutants, including particulate matter, volatile organic compounds (VOCs), and hazardous gases. The adoption of wireless communication, cloud-based data storage, and AI-driven analytics enables seamless integration of monitoring data with enterprise-level environmental management systems. These technological breakthroughs reduce operational costs, minimize maintenance requirements, and offer scalable solutions for large industrial complexes, further driving market growth.

Another critical growth driver is the increasing emphasis on corporate social responsibility (CSR) and community relations management. Industrial organizations are now more accountable for their environmental footprint, and transparent communication of air quality data to nearby communities has become standard practice. Fence line air quality monitoring systems provide verifiable, real-time data that can be shared with stakeholders, regulators, and the public, fostering trust and reducing the risk of litigation. Additionally, the rise of smart city initiatives and urban air quality management programs in developing economies is creating new opportunities for market expansion beyond traditional industrial applications, highlighting the broadening scope of this market.

From a regional perspective, North America currently dominates the fence line air quality monitoring market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, in particular, has witnessed significant investments in air quality monitoring infrastructure, driven by regulatory mandates and technological innovation. Europe’s market is bolstered by stringent environmental policies and strong industrial presence, while Asia Pacific is emerging as a high-growth region due to rapid industrialization, urbanization, and increasing government focus on air pollution control. Latin America and the Middle East & Africa are also experiencing steady growth, albeit from a smaller base, as regulatory frameworks mature and awareness of environmental health risks increases.

Component Analysis

The component segment of the fence line air quality monitoring market encompasses hardware, software, and services, each playing a vital role in the deployment and operation of comprehensive monitoring solutions. Hardware forms the backbone of these systems, comprising sensors, analyzers, data loggers, communication modules, and en

Abstract

The main purpose of the KPMS surveys is to provide data for the study of multiple aspects of household welfare and behavior, analysis of poverty, and understanding the effect of government policies on households.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

In order to expedite the survey process, NATSTATCOM used much of the same sample design and survey instruments as those used for the 1993 Baseline Survey. However, the Fall 1996-1998 KPMS surveys used a new sampling frame based on the Kyrgyz Household Registration System. This system was taken from the Census Posts intended for use by the first National Census of the Kyrgyz Republic. Using this system, NATSTATCOM updated the central household registration files effective January 1, 1996, and the information that was used for the sampling frame was as up to date as possible. The procedures followed in the stratification and identification of Primary Sampling Units (PSUs) were similar for all rounds of the KPMS as discussed below.

Formation of Strata

Initially the country was divided into seven (7) strata defined by oblasts (Oblasts are administrative divisions of the country which in turn are sub-divided in to Rayons) and by residence location (i.e. urban vs. rural) within oblasts. The rural portion of Bishkek oblast was combined with the rural portion of neighboring Chui oblast for stratification purposes as Bishkek has practically no rural population.

Selection of PSUs and Households

For the 1998 KPMS, a total of 255 PSUs (of which 178 were urban and 77 rural) were identified. The estimated total population was around 1.1 million of which about 421,000 was classified as urban. A minimum of 384 households per oblast was targeted in order to get a representative data at the oblast level11. This translated in to a targeted sample size of 2,688 households for the whole of the Kyrgyz Republic (i.e. 384*7 oblasts=2,688). These households were divided into urban (887 households) and rural (1,801 households). The overall projected response rate for the 1998 KPMS was also set at somewhat above 0.90. With an overall sampling rate of 1/336, this resulted in to a sample close to a target size of 3,000 households for the whole survey.

Once the strata and PSUs were formed and identified, selection of sample PSUs and households was then carried out in the following order:

1) Selection of large and small towns12 [Note: For the 1998 KPMS, large towns were defined as those with a population size of 41,125 or larger. Small towns are those with population less than 41,125. This number, according to a NATSTATCOM document was calculated as follows: n=4.7*350*25. This calculation was based on an estimated household size of 4.7, an estimated interval rate of 350 and an average work load per interviewer of 25 households. No further information is available regarding the bases of such an assumption. At the moment, we do not have information about the cut off number that separates large towns from small ones for the other two KPMS.]

2) Selection of Census Posts in urban areas

3) Selection of Ayil Kenshes (village authorities) and population points in rural areas, and

4) Selection of households from selected Census Posts and Ayil Kenshes. In the rural stratum of each oblast, villages were used as the listing units and within these listing units, equal probability sampling methods were used to select the ultimate sampling units (households). In urban areas, the centralized computer listings from various sources of household registration were used for the selection of households. These lists are categorized into four: Type 1 - Private house resident households listed by BTIs Type 2 - Public house residents listed with other organizations with dormitories only Type 3 - Public and private households listed by JSKs Type 4 - Public and private households listed by all other organizations. In some cases, private households were included in the last three public categories (Types 2, 3 and 4). However, only public households were selected from these types since it was believed that any private households listed in these category types were also included in the Type 1 category. The counts for Type 2, 3, and 4 lists were then adjusted based on the oblast estimates of all urban households.13 Prior to actual household sample selection, lists from types 2 to 4 were updated and adjusted to remove private households, so that any potential double eligibility was eliminated. Urban strata were then formed within each oblast based on type of household listing. In most cases, types had to be combined to form strata of a reasonable size.

Within the limits of rounding and requiring at least one sampling unit per stratum, the allocation of sampling units to urban strata was proportional to the number of households projected for that stratum after allowing for removal of duplicates (private households appearing on a BTI and other lists).

As for rural households, selection of urban households was done using systematic random sampling within each stratum except that more subdividing of urban lists was required before selecting the final list sample that defines each sampling unit.

Even though the list sources were identified and sampled using data as of January 1, 1996 (and using projections of unduplicated counts in some cases), the final listings were updated in the field just prior to the survey period. Therefore, the sample households in selected areas were drawn from the most current available listings.

Mode of data collection

Face-to-face [f2f]

Research instrument

The KPMS surveys were carried out using a household questionnaire and a community (population point) questionnaire. The household questionnaires were used to collect demographic information on the composition of the household, housing, household consumption including home production, as well as economic activities in agricultural and non-agricultural sectors. For each household member, individual level data on health, education, migration and labor was collected using the household questionnaires. Community questionnaires were used to collect price data and the presence of social services and infrastructure in the community (population point) where the sampled household is located.

The household questionnaire was extensive and required several hours of intense interviewing to gather all that was needed from each household and its embers. The household questionnaire was split into two parts. The first part was used to collect data through a face to face interview on household roster, dwelling, education, health, migration, etc. At the end of the first part, members who shop for food for the whole household and those who know most about income, expenditure and savings of other household members were identified and designated as respondents for the next part (second round). The second round of interview was administered two weeks after the first half and collected data on crops, food and animal products produced by the household, food expenditure and home produced food consumption.

Some sections of the household questionnaire such as those that deal with dwelling and expenditure information were administered to the person most knowledgeable of the family's overall expenditures, income and other finances as well as about the family's business activities and employment. In other sections, each adult in each sample household was interviewed individually. The information gathered from each household included extensive data on education, health, employment, migration, reproduction and reproductive health (for women aged 15 to 49), land use, expenditure, revenue and other financial matters, as well as anthropometric measurements (for children 5 years and younger). Information about children under 14 years of age was collected by asking the relevant questions to the adult household member who is primarily responsible for each child's care.

The community (Population Point) questionnaires were administered to each sample cluster. They were used to collect data on prices of goods and services, distance to schools, shopping and medical facilities, types of housing, commercial and private land use and availability of infrastructure.

HOUSEHOLD QUESTIONNAIRE

The KPMS household questionnaires generally contain 15 major sections, and each of these sections covers a separate aspect of household activity. In some cases, the section has sub-sections. These household questionnaires were designed to better assess the changing environment brought about by the advent of a market economy and to enable a more in depth analysis of topics such as housing, health, and education. The various sections of the KPMS household questionnaire are described below.The household questionnaires administered in the KPMS surveys are more or less similar with minor modifications and additions in the successive rounds of the KPMS.

POPULATION POINT QUESTIONNAIRE

The community (population point) questionnaire was used to collect information and data that are relevant to the community/population point where the household is located. The questionnaire was designed to be administered in the geographical area of each sample cluster. It was used to collect data regarding prices of goods and services in the local area and data on community infrastructure. Respondents to these questionnaires are those believed to be well informed members of the community that the interviewers identified by going to the rayon, city, oblast administration or other governmental agency located in the population point6. The