Traditional Survey Methods Research Articles

Comparing Chatbots and Online Surveys for (Longitudinal) Data Collection: An Investigation of Response Characteristics, Data Quality, and User Evaluation

ABSTRACT As chatbots are gaining more popularity than ever, they have recently been considered as interesting tools for survey administration in social science research. To explore this idea, we investigated the extent to which there are differences in response characteristics and data quality between a traditional, web-based survey and a conversational, chatbot-based survey (which we integrated in an instant messaging app). In addition, we zoomed into how respondents evaluate both survey modes. Using a longitudinal design, we also explored how response characteristics evolved over a period of two weeks. Overall, we did not find evidence that chatbots might be better survey administration tools than web surveys. On the contrary, the web survey often seemed to generate more favorable response characteristics and data quality. Finally, when it comes to user perceptions, we found that the chatbot survey was evaluated less favorably in terms of perceived enjoyment, usefulness, and security. Based on these results, we draw conclusions about whether chatbots can be considered as valid alternatives for traditional web survey methods.

Identification and Classification of Bus and Subway Passenger Travel Patterns in Beijing Using Transit Smart Card Data

Urban public transit has been rapidly developed in recent years. However, given increases in travel volume, the level of service still needs to be improved to meet the satisfaction of passengers. Transit service providers and researchers have focused on improving transit devices, but the service level of public transit has not yet been effectively improved, so more and more research is interested in analyzing the travel patterns of passengers. Compared with traditional survey methods, smart card collection systems—which can collect spatial-temporal information about passengers’ trips—are convenient for the study of bus and subway passengers’ travel patterns. However, the data provided by smart cards have not yet been fully explored. Therefore, this paper proposed a multistep methodology to gather information on the travel patterns of bus and subway passengers in Beijing, China. We conducted statistical analyses and used an unsupervised clustering method to study and classify passengers based on travel patterns. Four groups have been identified: standard commuters, flexible commuters, and two types of low-frequency passengers. Then, a comprehensive analysis was conducted. We also discussed the changes of passengers’ travel time consumption before and after the implementation of customized bus for high-frequency passengers. The analyses indicated that passengers’ travel patterns can provide useful information for transit service providers and can help improve the level of service of urban public transit by promoting the promulgation of local public transport policies and the implementation of customized services.

Comparing Effectiveness of AHDriFT Systems and Sherman Traps for Surveying Small Mammals in Northeastern Indiana

Abstract Traditional small mammal survey methods (e.g., Sherman traps) are beneficial in certain conditions but tend to require substantial effort and funds and can introduce various biases. The recently described adapted-Hunt drift fence technique (AHDriFT) camera trap system (i.e., camera traps combined with drift fences) can survey small terrestrial vertebrates and does not require much time in the field. Our objective was to compare the efficiency and effectiveness of AHDriFT systems and traditional Sherman traps for surveying small mammal communities. We conducted surveys with both methods in four sites of varying habitat types at the Eagle Marsh Nature Preserve (Fort Wayne, Indiana) from February to July in 2020 and May to August in 2021. We conducted 640 trap nights (one trap set on one calendar night) of Sherman trap surveys and 551 trap nights of AHDriFT system surveys. We captured 192 small mammals of three species with Sherman traps and obtained 532 images of unique small mammal individuals of seven species with AHDriFT systems. Our AHDriFT systems resulted in two times greater species richness (Z = −6.21, P < 0.01), 16 times greater species evenness (Z = −4.83, P < 0.01), and 23 times greater Shannon's diversity values (Z = −4.87, P < 0.01) than Sherman traps. The AHDriFT systems also documented the presence of four species that the Sherman traps did not (northern short-tailed shrew Blarina brevicauda, common shrew Sorex cinereus, long-tailed weasel Neogale frenata, eastern chipmunk Tamias striatus). Overall, AHDriFT system surveys provided 1.5–5 times more observations per dollar spent and required 90% less time in the field than Sherman trap surveys. These results suggest that AHDriFT systems may be a more efficient and effective method of surveying small mammal communities.

Survey Déjà Vu: Lessons Learned from the Archaeological Re-mapping of a Métis Overwintering Settlement

Although the advantages of archaeological remote sensing have long been known, the techniques have still not been fully incorporated into standard archaeological practice. Drawing upon the example of an archaeological remote sensing survey conducted in April 2022 and subsequent excavation in July 2022 at the Chimney Coulee site (DjOe-6) in Saskatchewan, we demonstrate the value of the integration of remote sensing methods early and throughout an entire project. Over the span of five days, we were able to use drone-based light detection and ranging (LiDAR) and orthoimagery, ground-penetrating radar (GPR), and magnetic gradiometry alongside more traditional archaeological survey methods to survey the site and locate two probable late nineteenth-century Métis cabins. The use of remote sensing techniques allowed for the efficient identification of future excavation areas and comparisons to previous mapping work and generated new questions about the site. This paper provides a methodological example of non-invasive archaeological survey for non-specialists and demonstrates how students and early career researchers can play an important role in the advancement of Canadian archaeology by experimenting with new ways of conducting archaeological survey and mapping.

Experimental methods in sociology used in sensitive questions: List experiment example

This article explores experimental methods in sociology that are used to address sensitive questions. Sensitive questions are those that involve socially stigmatized or taboo subjects, such as illegal activities, personal beliefs, and sexual behavior. The use of traditional survey methods to investigate sensitive questions may lead to response bias or social desirability bias. To overcome these limitations, scientists have developed innovative experimental methods, such as the list experiment. The list experiment is a survey-based technique that provides respondents with a list of items and asks them how many items on the list they have experienced or endorse. Unlike direct questioning, this method allows respondents to express their opinions anonymously and reduces social desirability bias. Moreover, the list experiment can estimate the prevalence of sensitive behaviors or attitudes without revealing individual responses, which makes it particularly useful in studying politically or socially sensitive topics. This article describes the theoretical foundations, design, and implementation of the list experiment. With the help of the VOSviewer program, a bibliographic analysis was carried out based on Scopus database. At the same time, it discusses the advantages and limitations of this method compared to other experimental techniques and highlights some of its practical applications in sociological research. Overall, this article provides a comprehensive overview of the list experiment and its contribution to the advancement of sociological research.

Extracting epidemiological knowledge of shrimp farmers applying participatory appraisal tools and mobile phone-assisted survey to identify and solve the problems of WSD

Participatory epidemiologists have emphasized that farmers have rich and detailed knowledge about the diseases of the animals they rear and the risk factors responsible for the diseases. Based on this conception a step-wise method where collegial participation of researchers and farmers was followed to gather qualitative and quantitative information to identify the possible risk factors of white spot disease (WSD) of shrimp. This proposed method consists of two phases: participatory phase that includes sampling of relevant farmers and identifying possible risk factors; and conducting survey and data analysis phase that includes mobile phone-assisted survey and statistical analysis. The advantages compared to traditional survey methods are that this method: i) brings researchers and shrimp farmers together in a participatory platform in identifying various risk factors associated with WSD, which is not possible by researcher-only surveys; ii) feeds the data of risk factors/variables to the mobile-phone assisted survey, in order to enhance the data quality through direct participation by the farmers and using mobile phones for data entry; and iii) supports multivariate statistical analysis, which is highly effective in identifying the relationships between potential risk factors and WSD in shrimp. Farm operator, the type of fertilizer used, water source (direct natural), presence or absence of a reservoir, and frequency of water exchange were the significant variables/risk factors found from the multivariate analysis most likely to be associated with WSD prevalence. Analysis of the collected data reveals that the major constraining variables identified by this participatory study were related to water management measures. Therefore, the WSD affected shrimp farmers do not need any expensive inputs from outside their communities but can solve the problem of WSD participatory manner with other stakeholders. A participatory model, as we proposed based on the results indicates that, DoF as the key stakeholder can play a leading role for organizing cluster of farmers and identify other stakeholders to solve the problems of WSD.

Estimating Ground Elevation in Coastal Dunes from High-Resolution UAV-LIDAR Point Clouds and Photogrammetry

Coastal dune environments play a critical role in protecting coastal areas from damage associated with flooding and excessive erosion. Therefore, monitoring the morphology of dunes is an important coastal management operation. Traditional ground-based survey methods are time-consuming, and data must be interpolated over large areas, thus limiting the ability to assess small-scale details. High-resolution uncrewed aerial vehicle (UAV) photogrammetry allows one to rapidly monitor coastal dune elevations at a fine scale and assess the vulnerability of coastal zones. However, photogrammetric methods are unable to map ground elevations beneath vegetation and only provide elevations for bare sand areas. This drawback is significant as vegetated areas play a key role in the development of dune morphology. To provide a complete digital terrain model for a coastal dune environment at Topsail Hill Preserve in Florida’s panhandle, we employed a UAV, equipped with a laser scanner and a high-resolution camera. Along with the UAV survey, we conducted a RTK–GNSS ground survey of 526 checkpoints within the survey area to serve as training/testing data for various machine-learning regression models to predict the ground elevation. Our results indicate that a UAV–LIDAR point cloud, coupled with a genetic algorithm provided the most accurate estimate for ground elevation (mean absolute error ± root mean square error, MAE ± RMSE = 7.64 ± 9.86 cm).

Transformer attention network and unmanned aerial vehicle hyperspectral remote sensing for grassland rodent pest monitoring research

The proliferation of grassland rodents has aggravated the degradation process of desert grassland. Additionally, it carries many viruses that threaten human and animal health. Accurate spatial distribution in grassland between rodent populations, vegetation, and bare soil is essential for developing rodent control measures. However, the traditional survey method of grassland rodent pest information is time-consuming and costly, and the period is long. In addition, satellite remote sensing cannot meet the accuracy requirements for the identification of grassland rat holes due to the limitation of spatial resolution. To realize intelligent grassland rodent infestation monitoring, this paper adopts an unmanned aerial vehicle hyperspectral remote sensing platform for data acquisition. Meanwhile, a transformer attention network (TAN) is proposed for grassland rodent infestation information extraction. The network adopts a two-stage feature extraction structure that effectively improves the classification performance of the model. In each stage, first, local features are extracted by a fixed convolution kernel to enhance detailed texture features; second, the extracted local features are refined using the contour convolution module to enrich feature information at the edges of the feature map; finally, the transformer attention module is used to focus on the global pixels, thus suppressing background information and enhancing effective information output. The results show that the overall accuracy (OA), average accuracy, and kappa coefficient of the TAN network can reach 97.71%, 98.44%, and 0.9538, respectively. Compared with several networks, such as two-dimensional CNN, three-dimensional CNN, HybridSN, and CTN, the OA values of the TAN network were improved by 2.59%, 2.45%, 2.94%, and 1.03%, respectively. The results of this study effectively improve the efficiency of the grassland rodent information survey and provide a solid theoretical basis for the investigation and statistics of grassland rodent infestation.

Vignette Research Methodology: An Essential Tool for Quality Improvement Collaboratives.

Variation in patient outcomes among institutions and within institutions is a major problem in healthcare. Some of this variation is due to differences in practice, termed practice variation. Some practice variation is expected due to appropriately personalized care for a given patient. However, some practice variation is due to the individual preference or style of the clinicians. Quality improvement collaboratives are commonly used to disseminate quality care on a wide scale. Practice variation is a notable barrier to any quality improvement effort. A detailed and accurate understanding of practice variation can help optimize the quality improvement efforts. The traditional survey methods do not capture the complex nuances of practice variation. Vignette methods have been shown to accurately measure the actual practice variation and quality of care delivered by clinicians. Vignette methods are cost-effective relative to other methods of measuring quality of care. This review describes our experience and lessons from implementing vignette research methods in quality improvement collaboratives in California neonatal intensive care units. Vignette methodology is an ideal tool to address practice variation in quality improvement collaboratives, actively engage a large number of participants, and support more evidence-based practice to improve outcomes.

Zoonyms from the Vilnius Region as a reflection of linguistic and cultural contacts

The issue of nomenclature for mixed breed animals has been taken up by Polish researchers many times and its significance for onomastic and dialectal research has been emphasised often enough. With respect to the North-Eastern Borderlands, sparse studies have been dedicated to it so far. Therefore, this paper discusses linguistic and cultural contacts reflected in proper names of two species of mixed breed animals in the Vilnius Region. The material was collected using the traditional and online survey method. This article presents the phonetic and morphological characteristics typical of the Polish of the Borderlands, lexical features, and nomenclature influences that were discovered while analysing zoonyms. It discusses the influences of the Lithuanian, Russian, and Belarusian languages on the nomenclature for animals living in households of people of Polish descent in the Vilnius Region

Impact of Modern Technologies on the Organization of the Cadastral Data Modernization Process

Land surface and environmental data (cadastral data) are extremely important in the functioning of the country and society. Upgrading the data is fundamental. Methods of traditional surveying (TM) or using modern remote data acquisition methods (PhM) are used for this purpose. The aim of the study is to compare the process of upgrading space and environmental data made by traditional methods and using modern remote data collection methods. The study established the following research hypotheses: (1) the election of the method of performing the cadastral data modernization process to consider effectiveness, productivity, profitability, quality (accuracy), reliability, and efficiency; (2) technical factors, as well as employee well-being and commitment, are equivalent motivators for the election of the cadastral data modernization method; (3) modern survey technologies using photogrammetric images are more efficient than traditional survey methods. The process evaluation methodology was tested on two objects located in Poland. The analyses considered both technical aspects and the comfort of the process contractors. The results showed that despite the higher unit price per cadastral plot (TM 180 PLN/cadastral plot, PhM 190 PLN/cadastral plot), the remote methods require less time commitment (TM-86 days; PhM-50 days) and involve reduced business travel (TM-65 days; PhM-29 days). The comfort of working with modern methods (PhM) is higher than with traditional measurement methods. In total, considering all the parameters studied, traditional methods required about 33% more commitment than modern remote methods of collecting surface and environmental data collection. Modern data acquisition methods are friendly to process contractors but gain less public acceptance than traditional methods (the level of border non-acceptance is higher in PhM methods than in TM (TM-3, Phm-8).

An online path planning algorithm for autonomous marine geomorphological surveys based on AUV

This paper proposed a data-driven bi-pattern (DDBP) path planning algorithm for ocean geomorphological surveys based on Autonomous Underwater Vehicles (AUVs). When an AUV conducts surveys in unknown areas, it uses the observation data of real-time side-scan sonar to conduct environment modeling to drive independent online path re-planning (PRP) according to the feature density of the interesting targets. Based on the DDBP algorithm, the AUV can autonomously focus on regions with rich target distribution and deviate from regions with sparse target distribution without prior knowledge of the task region. The quality and efficiency of the AUV-based surveys can be improved by focusing on the underwater detection area with high feature density. The DDBP algorithm includes two patterns: rough and fine scan, and the corresponding planning pattern is selected according to the distribution of the detected targets. AUV performs online PRP in the corresponding pattern according to the pre-identified strategy set. We conducted simulation experiments and selected sand waves and fish reefs as natural and artificial structures to conduct typical marine survey tests. Compared with the traditional marine survey method, the survey efficiency was increased by 33.6% and 29.6%, respectively, in the two DDBP survey experiments for sand waves; the efficiency increased by 32.9% and 36.7%, respectively, in the two groups of DDBP survey experiments on artificial reefs. The proposed general technical framework for online path planning driven by real-time observation data has good application prospects in underwater archaeology, rapid understanding of specific targets on the seafloor, and search of specific targets.

Graphical Documentation of the Historical City of Cagliari (Italy)

Abstract The documentation of the urban historical heritage is the preliminary step to identify the correct strategies for its protection and enhancement. To achieve this aim it is necessary to acquire a graphic documentation at the different scales of detail required by a multi-layered system as the historical landscape; this complexity can now be explored integrating traditional and digital survey methods and representation methodologies. Traditional methods, in particular, are characterized by a direct contact with places; this condition guarantee a deepest perception and a total immersion in the landscape integrating and giving quality to the considerable amount of data offered by recent technologies; this approach has been applied in the survey of the historical city of Cagliari in which the medieval districts of Marina, Stampace, Villanova and Castello retain a great part of the modern walled system built since the fourteenth century until the first half of the nineteenth century and a lot of interesting buildings designed for civil, public and religious use.

Mountainous village relocation planning with 3D GIS virtual imaging space model and neural network

ABSTRACT China’s mountainous village relocation planning faces the problems of extensive survey ranges and high travel costs, and current planning still depends on traditional field visits and census data survey method. To improve efficiency and control costs, digital survey methods are proposed to support efficient data collection and produce data with accurate spatial reference . This research discusses a specific survey method for relocation planning using virtual imaging space modeling based on 3D GIS combined with neural network algorithm. A digital village model of a pilot village is established by oblique photography and WEBGIS technology, while the improved Mask R-CNN algorithm is adopted to identify and classify different buildings in the village. A relocation plan including the number of buildings in each class and the reclamation area is provided and the cost and value of the plan is fully evaluated. The results demonstrate that compared with the traditional survey method, the proposed method of digital survey can better address the issue of remote mountain areas, including provide more accurate, complete and sustainable information, make the surveying and planning more convenient, efficient and smart and improve the objectivity of village relocation planning.

A colonial-nesting seabird shows no heart-rate response to drone-based population surveys

Aerial drones are increasingly being used as tools for ecological research and wildlife monitoring in hard-to-access study systems, such as in studies of colonial-nesting birds. Despite their many advantages over traditional survey methods, there remains concerns about possible disturbance effects that standard drone survey protocols may have on bird colonies. There is a particular gap in the study of their influence on physiological measures of stress. We measured heart rates of incubating female common eider ducks (Somateria mollissima) to determine whether our drone-based population survey affected them. To do so, we used heart-rate recorders placed in nests to quantify their heart rate in response to a quadcopter drone flying transects 30 m above the nesting colony. Eider heart rate did not change from baseline (measured in the absence of drone survey flights) by a drone flying at a fixed altitude and varying horizontal distances from the bird. Our findings suggest that carefully planned drone-based surveys of focal species have the potential to be carried out without causing physiological impacts among colonial-nesting eiders.

Using fiber-optic distributed temperature sensing in fisheries applications: An example from the Ozark Highlands

Studies of thermal selection by organisms, including fishes, are common and provide data that are useful for conservation and management. Advances in temperature sensing technology have improved these studies; however, the benefits of new technology (e.g., increased accuracy and greater deployment flexibility) should be carefully considered and compared to disadvantages (e.g., higher costs and training requirements). Fiber-optic distributed temperature sensing (FO-DTS) has become more common in aquatic applications and may provide a novel and useful method of relating thermal patchiness to habitat selection by fishes or other aquatic organisms. We present a case study using FO-DTS to conduct a microhabitat-scale resource selection study using stream fishes of the Ozark Highland ecoregion in the south-central United States. We describe the setup and deployment of FO-DTS and how it was integrated into traditional microhabitat survey methods at three stream sites that were repeatedly surveyed over consecutive days. We successfully used FO-DTS to characterize thermal selection by Neosho Bass Micropterus velox at our sites and conclude that the technology would be applicable to similar, microhabitat-scale evaluations. We then compare costs, benefits, and disadvantages of FO-DTS to other sensing methods that could have been used to complete our study. We found that FO-DTS provided accurate measures and greater coverage compared to most alternatives but that equipment costs were far greater. We provide suggestions for additional fisheries applications where FO-DTS may be useful while acknowledging that in some instances, the upfront costs of the technology may outweigh the potential benefits.

Application of LiDAR Survey Technology in Space Launch Sites

LiDAR has the advantages of small size, high imaging accuracy, high resolution, all-day survey, and strong adaptability to complex environments. With the continuous improvement of technical performance, LiDAR is widely used in aerospace, mapping, military, and civil fields. In this study, the principle of LiDAR survey technology was introduced, and the key technology and application method of using LiDAR to measure the initial section of rocket launch were discussed in detail; a LiDAR survey system was designed on this basis. Afterward, the feasibility and rationality of the survey system and survey method were verified by rocket launch tests. The results show that compared with the traditional survey methods, LiDAR survey technology has obvious advantages in terms of survey accuracy, automation, and real-time visualization of survey data, and it can greatly reduce labor intensity and improves work efficiency, which provides an important reference for the design and application mode of LiDAR survey system in space launch sites.

Species diversity of coral reef fishes around the West Island of Sanya City, South China Sea, based on environmental DNA.

West Island is the second largest island in the Hainan Province, China and its surrounding sea area has a well-preserved coral reef ecosystem and high species diversity of coral reef fishes. Undoubtedly, coral reef fishes and coral reefs have complex symbiotic relationships and fish species diversity should reflect the healthy status of coral reef ecosystems. Environmental DNA (eDNA) is a useful and sensitive tool to detect fish species and causes less environmental damage than traditional fish survey methods. This paper investigated coral reef fish species of West Island, Hainan Province, China, based on eDNA and provided scientific data for understanding and protection of the coral reef ecosystem of the South China Sea. The sea area surrounding West Island is the most important component of the coral reef ecosystem in the northern part of the South China Sea, which is also an essential part of the largest National Coral Reef Reserve in China. However, complete data of fish species distributed in this region have been a long-term gap. This study provides information on 41 fish species belonging to 28 genera, 16 families and three orders in this sea area and is the first complete record of coral reef fishes surrounding the West Island. In addition, the information of Molecular Operational Taxonomic Units (MOTUs) for taxon identification were also provided and it could contribute to building specific eDNA taxonomy database of coral reef fishes of the South China Sea. The study includes three datasets, with aspects of fish taxon-occurrences, MOTUs sequences and information of environmental indicators surrounding West Island, Hainan Province, China. The "fish taxon occurrences" dataset presents records involving taxonomic, distribution, habitat condition, latitude and longitude of 41 coral reef fish species detected, based on eDNA, the "MOTUs sequences" dataset provides MOTUs sequences and their abundance of 31 species detected and the "information of environmental indicators" dataset presents records of transparency, temperature, water pressure, dissolved oxygen, electrical conductivity, hydrogen and redox potential measured from five sampling localities.

A novel method for cliff vegetation estimation based on the unmanned aerial vehicle 3D modeling.

The cliff ecosystem is one of the least human-disturbed ecosystems in nature, and its inaccessible and often extreme habitats are home to many ancient and unique plant species. Because of the harshness of cliff habitats, their high elevation, steepness of slopes, and inaccessibility to humans, surveying cliffs is incredibly challenging. Comprehensive and systematic information on cliff vegetation cover is not unavailable but obtaining such information on these cliffs is fundamentally important and of high priority for environmentalists. Traditional coverage survey methods—such as large-area normalized difference vegetation index (NDVI) statistics and small-area quadratic sampling surveys—are not suitable for cliffs that are close to vertical. This paper presents a semi-automatic systematic investigation and a three-dimensional reconstruction of karst cliffs for vegetation cover evaluation. High-resolution imagery with structure from motion (SFM) was captured by a smart unmanned aerial vehicle (UAV). Using approximately 13,000 records retrieved from high-resolution images of 16 cliffs in the karst region Guilin, China, 16 models of cliffs were reconstructed. The results show that this optimized UAV photogrammetry method greatly improves modeling efficiency and the vegetation cover from the bottom to the top of cliffs is high-low-high, and very few cliffs have high-low cover at the top. This study highlights the unique vegetation cover of karst cliffs, which warrants further research on the use of SFM to retrieve cliff vegetation cover at large and global scales.

Image Extraction of Tailings Pond Guided by Artificial Intelligence Support Vector Machine

The large number of tailings ponds in my country, coupled with various man-made and natural factors in recent years, leads to the frequent occurrence of tailings pond accidents, causing serious harm. Mastering the number and distribution of tailings ponds is of great significance to prevent tailings pond accidents and carry out emergency management of tailings ponds. For the identification and monitoring of tailings ponds, the traditional survey methods are mainly based on ground surveys, and it is difficult to achieve large-scale and high-frequency detection. With the development of artificial intelligence technology, based on the support vector machine (SVM) method, the automatic extraction of remote sensing image (RSI) information has been realized, and remarkable achievements have been made in the field of remote sensing. This paper takes the tailings pond as the research goal, and based on the analysis of the characteristics of the tailings pond in the RSI, the method of SVM target detection is used to realize the rapid and high-precision identification of the tailings pond. The main contents of this paper are as follows: (1) The accident risk of tailings ponds at home and abroad and the status quo of tailings pond monitoring technology are introduced. (2) The relevant theory of SVM is learned, and the kernel function and corresponding parameter selection method of SVM are discussed in a multiclassification problem. (3) On the basis of determining the kernel function, parameters, and features, the trained model is compared with the original model. The results show that the SVM detection model proposed in this paper has excellent performance in tailings pond image recognition.