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Students in geographic information systems and science (GIS) require significant experience outside of spatial analysis, cartography, and other traditional geographic topics. Computer science knowledge, skills, and practices exist as essential components of GIS practice, but coursework in this area is not universally offered in geography or GIS degrees. To support those interested in developing such courses, this paper describes the design and implementation of a server-focused course in WebGIS at Texas A&M University. We provide an in-depth discussion of the equipment and resources required to build and operate an on-premise CyberGIS server infrastructure suitable for supporting such classes, providing comparisons with an equivalent solution built on Amazon Web Services (AWS). We consider the comparative costs of these systems, including benefits and drawbacks of each. In comparing these deployment options, we outline the technical expertise, monetary investments, operational expenses, and organizational strategies necessary to run server-based CyberGIS courses. Finally, we reflect on assignments and feedback from students and consider their experiences in a course of this nature. This article provides a resource for GIS instructors, academic departments, or other academic units to consider during infrastructure investment, curriculum redesign, the addition of courses in degree plans, or for the development of CyberGIS components.

Sufficient and reliable health care access is necessary for people to be able to maintain good health. Hence, investigating the uncertainty embedded in the temporal changes of inputs would be beneficial for understanding their impact on spatial accessibility. However, previous studies are limited to implementing only the uncertainty of mobility, while health care resource availability is a significant concern during the coronavirus disease (COVID‐19) pandemic. Our study examined the stochastic distribution of spatial accessibility under the uncertainties underlying the availability of intensive care unit (ICU) beds and ease of mobility in the Greater Houston area of Texas. Based on the randomized supply and mobility from their historical changes, we employed Monte Carlo simulation to measure ICU bed accessibility with an enhanced two‐step floating catchment area (E2SFCA) method. We then conducted hierarchical clustering to classify regions of adequate (sufficient and reliable) accessibility and inadequate (insufficient and unreliable) accessibility. Lastly, we investigated the relationship between the accessibility measures and the case fatality ratio of COVID‐19. As result, locations of sufficient access also had reliable accessibility; downtown and outer counties, respectively, had adequate and inadequate accessibility. We also raised the possibility that inadequate health care accessibility may cause higher COVID‐19 fatality ratios. [ABSTRACT FROM AUTHOR]

The implementation of temporal dynamic variables improves the accuracy of spatial accessibility measurements. However, in previous studies, the temporal dynamics were partially incorporated, and a few snapshots were subjectively selected to demonstrate temporal changes of spatial accessibility, which may not represent the entire variation. In this study, we proposed a conceptual framework to leverage spatial accessibility temporal fluctuation, facilitating decision-making. Not only was the full implementation of time-dependent inputs, but the framework also employed the Gaussian two-step floating catchment area (G2SFCA) method and measured hourly spatial accessibility over 24 hours. Then, a temporal clustering with K-means and hierarchical clustering methods was performed, detecting a few distinctive temporal changes. Lastly, the significance of dynamic accessibility measurement and temporal clustering was validated using Pearson's correlations. We took electric vehicle (EV) charging stations in Seoul, South Korea, as a case study. The results presented that neglecting temporal dynamics could fail to predict accessibility during the daytime. Additionally, temporal clustering summarized the 24-hour changes of accessibility into five temporal phases and showed the need for additional resources for insufficient accessibility locations in the afternoon. Consequently, our framework elicited the temporal changes of accessibility measures and identified a specific space and time for supplementary infrastructure. [ABSTRACT FROM AUTHOR]

While the spatial resolution of exposure surfaces has greatly improved, our ability to locate people in space remains a limiting factor in accurate exposure assessment. In this case‐control study, two approaches to geocoding participant locations were used to study the impact of geocoding uncertainty on the estimation of ambient pesticide exposure and breast cancer risk among women living in California's Central Valley. Residential and occupational histories were collected and geocoded using a traditional point‐based method along with a novel area‐based method. The standard approach to geocoding uses centroid points to represent all geocoded locations, and is unable to adapt exposure areas based on geocode quality, except through the exclusion of low‐certainty locations. In contrast, area‐based geocoding retains the complete area to which an address matched (the same area from which the centroid is returned), and therefore maintains the appropriate level of precision when it comes to assessing exposure by geography. Incorporating the total potential exposure area for each geocoded location resulted in different exposure classifications and resulting odds ratio estimates than estimates derived from the centroids of those same areas (using a traditional point‐based geocoder). The direction and magnitude of these differences varied by pesticide, but in all cases odds ratios differed by at least 6% and up to 35%. These findings demonstrate the importance of geocoding in exposure estimation and suggest it is important to consider geocode certainty and quality throughout exposure assessment, rather than simply using the best available point geocodes. Plain Language Summary: Understanding the relationship between environmental exposures and cancer development is limited by how precisely we can locate people. While ideally all estimates would be based on building‐level precision, epidemiologic research must accommodate varying levels of locational accuracy, and is dependent on input address data quality (often patient addresses). This study uses traditional point‐based geocoding and a novel method of geocoding (area‐based) to estimate the relationship between ambient pesticide exposure and breast cancer. Although a "point" representing a geocoded location implies precision, point coordinates can be based on anything from an exact building centroid to an entire city and may miss relevant exposure for larger areas. Using area‐based geocoding, exposure estimation for an address resolved only to its ZIP Code is based on the entire ZIP Code area. We identified more individuals with potential pesticide exposure using area‐based geocoding. Importantly, the proportion of exposed cases and controls was inconsistent across geocoding methods and varied by pesticide, resulting in changes in the estimated exposure‐disease relationship. Geocoding quality plays a critical role in environmental exposure research, and misclassification may not be consistent or readily predictable. Methods incorporating spatial uncertainty (e.g., area‐based geocoding), may shed more light on this issue and support improvements. Key Points: Area‐based geocoding includes the precise area of uncertainty on an address‐by‐address basis, which is not possible using current methodsGeocode positional error can bias exposure and associated risk estimates in unpredictable waysArea‐based geocoding incorporates heterogeneity of the exposure surface that likely plays a role in the magnitude and direction of biases [ABSTRACT FROM AUTHOR]

Abstract Background The process of geocoding produces output coordinates of varying degrees of quality. Previous studies have revealed that simply excluding records with low-quality geocodes from analysis can introduce significant bias, but depending on the number and severity of the inaccuracies, their inclusion may also lead to bias. Little quantitative research has been presented on the cost and/or effectiveness of correcting geocodes through manual interactive processes, so the most cost effective methods for improving geocoded data are unclear. The present work investigates the time and effort required to correct geocodes contained in five health-related datasets that represent examples of data commonly used in Health GIS. Results Geocode correction was attempted on five health-related datasets containing a total of 22,317 records. The complete processing of these data took 11.4 weeks (427 hours), averaging 69 seconds of processing time per record. Overall, the geocodes associated with 12,280 (55%) of records were successfully improved, taking 95 seconds of processing time per corrected record on average across all five datasets. Geocode correction improved the overall match rate (the number of successful matches out of the total attempted) from 79.3 to 95%. The spatial shift between the location of original successfully matched geocodes and their corrected improved counterparts averaged 9.9 km per corrected record. After geocode correction the number of city and USPS ZIP code accuracy geocodes were reduced from 10,959 and 1,031 to 6,284 and 200, respectively, while the number of building centroid accuracy geocodes increased from 0 to 2,261. Conclusion The results indicate that manual geocode correction using a web-based interactive approach is a feasible and cost effective method for improving the quality of geocoded data. The level of effort required varies depending on the type of data geocoded. These results can be used to choose between data improvement options (e.g., manual intervention, pseudocoding/geo-imputation, field GPS readings).

Copyright of Professional Geographer is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: Hurricane Harvey (2017) forced the closure of hemodialysis centers across Harris County, Texas (USA) disrupting the provision of dialysis services. This study aims to estimate the percentage of hemodialysis clinics flooded after Harvey, to identify the proportion of such clinics located in high-risk flood zones, and to assess the sensitivity of the Federal Emergency Management Agency (FEMA) Flood Insurance Rate Maps (FIRMs) for estimation of flood risk. Methods: Data on 124 hemodialysis clinics in Harris County were extracted from Medicare.gov and geocoded using ArcGIS Online. The FIRMs were overlaid to identify the flood zone designation of each hemodialysis clinic. Results: Twenty-one percent (26 of 124) of hemodialysis clinics in Harris County flooded after Harvey. Of the flooded clinics, 57.7% were in a high-risk flood zone, 30.8% were within 1km of a high-risk flood zone, and 11.5% were not in or near a high-risk flood zone. The FIRMs had a sensitivity of 58%, misidentifying 42% (11 of 26) of the clinics flooded. Conclusion: Hurricanes are associated with severe disruptions of medical services, including hemodialysis. With one-quarter of Harris County in the 100-year floodplain, projected increases in the frequency and severity of disasters, and inadequate updates of flood zone designation maps, the implementation of new regulations that address the development of hemodialysis facilities in high-risk flood areas should be considered. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Geographer is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cervical cancer is widely preventable through screening, but little is known about the duration of protection offered by a negative screen in North America. A case–control study was conducted with records from population‐based registries in New Mexico. Cases were women diagnosed with cervical cancer in 2006–2016, obtained from the Tumor Registry. Five controls per case from the New Mexico HPV Pap Registry were matched to cases by sex, age and place of residence. Dates and results of all cervical screening and diagnostic tests since 2006 were identified from the pap registry. We estimated the odds ratio of nonlocalized (Stage II+) and localized (Stage I) cervical cancer associated with attending screening in the 3 years prior to case‐diagnosis compared to women not screened in 5 years. Of 876 cases, 527 were aged 25–64 years with ≥3 years of potential screening data. Only 38% of cases and 61% of controls attended screening in a 3‐year period. Women screened in the 3 years prior to diagnosis had 83% lower risk of nonlocalized cancer (odds ratio [OR] = 0.17, 95% CI: 0.12–0.24) and 48% lower odds of localized cancer (OR = 0.52, 95% CI: 0.38–0.72), compared to women not screened in the 5 years prior to diagnosis. Women remained at low risk of nonlocalized cancer for 3.5–5 years after a negative screen compared to women with no negative screens in the 5 years prior to diagnosis. Routine cervical screening is effective at preventing localized and nonlocalized cervical cancers; 3 yearly screening prevents 83% of nonlocalized cancers, with no additional benefit of more frequent screening. Increasing screening coverage remains essential to further reduce cervical cancer incidence. What's new? Screening is an effective means of preventing cervical cancer in women. However, while a negative screening result affords protection against cervical cancer, little is known about how long this protection lasts or when a woman should be screened again. Here, the authors estimate the impact of cervical screening at a state‐wide level, linking a U.S. population‐based screening registry with a SEER cancer registry. The results show that screening once every three years prevents 83 percent of stage 2+ cervical cancers, with no additional benefit from more frequent screening. The findings may help improve adherence to U.S. screening guidelines. [ABSTRACT FROM AUTHOR]

Various network model creation algorithms have been introduced to demonstrate a better approximation of the actual walking pattern and to provide a better wayfinding guide. However, it is under‐investigated which algorithm creates the most appropriate indoor navigation network model in the context of wayfinding applications. Due to the lack of discussion, some studies unconsciously extended an algorithm designed for creating an outdoor navigation network model to indoor space applications. This is problematic because indoor space has different spatial contexts from outdoor space, such as non‐linear space and no‐designated walking space. Our solution is to select five well‐known algorithms that have been introduced, to reproduce the algorithm for the automated construction of the indoor navigation network model, and to evaluate the applicability of algorithms for indoor wayfinding applications. This article compares the quality of wayfinding results from the output of the indoor navigation network model against two criteria: route efficiency (i.e., length) and route simplicity (i.e., number of directions). Our statistical analysis illustrates that the visibility graph algorithm is the most appropriate for indoor wayfinding applications. [ABSTRACT FROM AUTHOR]

Reverse geocoding, which transforms machine‐readable GPS coordinates into human‐readable location information, is widely used in a variety of location‐based services and analysis. The output quality of reverse geocoding is critical because it can greatly impact these services provided to end‐users. We argue that the output of reverse geocoding should be spatially close to and topologically correct with respect to the input coordinates, contain multiple suggestions ranked by a uniform standard, and incorporate GPS uncertainties. However, existing reverse geocoding systems often fail to fulfill these aims. To further improve the reverse geocoding process, we propose a probabilistic framework that includes: (1) a new workflow that can adapt all existing address models and unitizes distance and topology relations among retrieved reference data for candidate selections; (2) an advanced scoring mechanism that quantifies characteristics of the entire workflow and orders candidates according to their likelihood of being the best candidate; and (3) a novel algorithm that derives statistical surfaces for input GPS uncertainties and propagates such uncertainties into final output lists. The efficiency of the proposed approaches is demonstrated through comparisons to the four commercial reverse geocoding systems and through human judgments. We envision that more advanced reverse geocoding output ranking algorithms specific to different application scenarios can be built upon this work. [ABSTRACT FROM AUTHOR]

Maps are well recognized as an effective means of presenting and communicating health data, such as cancer incidence and mortality rates. These data can be linked to geographic features like counties or census tracts and their associated attributes for mapping and analysis. Such visualization and analysis provide insights regarding the geographic distribution of cancer and can be important for advancing effective cancer prevention and control programs. Applying a spatial approach allows users to identify location-based patterns and trends related to risk factors, health outcomes, and population health. Geographic information science (GIScience) is the discipline that applies Geographic Information Systems (GIS) and other spatial concepts and methods in research. This review explores the current state and evolution of GIScience in cancer research by addressing fundamental topics and issues regarding spatial data and analysis that need to be considered. GIScience, along with its health-specific application in the spatial epidemiology of cancer, incorporates multiple geographic perspectives pertaining to the individual, the health care infrastructure, and the environment. Challenges addressing these perspectives and the synergies among them can be explored through GIScience methods and associated technologies as integral parts of epidemiologic research, analysis efforts, and solutions. The authors suggest GIScience is a powerful tool for cancer research, bringing additional context to cancer data analysis and potentially informing decision-making and policy, ultimately aimed at reducing the burden of cancer. [ABSTRACT FROM AUTHOR]

Traffic collisions have been well acknowledged as a significant threat to public health, closely related to human driving errors. This study introduces an innovative approach to investigate spatiotemporal distributions of individualized driving errors and to characterize hazardous driving scenes, in which drivers are more prone to make driving mistakes. We first create a multi‐feature‐fusion framework to extract driving errors using smartphone sensors. Then, the detected errors are geo‐statistically analyzed with road networks and driving trajectories to identify driving error hotspots. We next construct a "scenic tuple" for representing the occurrence of driving errors. Finally, the individualized hazardous driving scenes are extracted by mining a long‐term collection of scenic tuples. Results demonstrate that our proposed approach can effectively identify driving errors. Additionally, the spatiotemporal patterns of driving mistakes can be identified from the individualized hazardous driving scenes, which has the potential to aid in reducing driving risks. [ABSTRACT FROM AUTHOR]

Geocoding has become a routine task for many research investigations to conduct spatial analysis. However, the output quality of geocoding systems is found to impact the conclusions of subsequent studies that employ this workflow. The published development of geocoding systems has been limited to the same set of interpolation methods and reference data sets for quite some time. We introduce a novel geocoding approach utilizing object detection on remotely sensed imagery based on a deep learning framework to generate rooftop geocoding output. This allows geocoding systems to use and output exact building locations without employing typical geocoding interpolation methods or being completely limited by the availability of reference data sets. The utility of the proposed approach is demonstrated over a sample of 22,481 addresses resulting in significant spatial error reduction and match rates comparable to typical geocoding methods. For different land‐use types, our approach performs better on low‐density residential and commercial addresses than on high‐density residential addresses. With appropriate model setup and training, the proposed approach can be extended to search different object locations and to generate new address and point‐of‐interest reference data sets. [ABSTRACT FROM AUTHOR]

CyberGIS is an interdisciplinary field that merges components of cyber‐infrastructure, geographic information science, and spatial analysis. This fusion combines the technical capabilities of advanced cyber‐infrastructure with the spatial analysis capabilities of GIS. How expert GIS practitioners perceive, use, and value the various components of CyberGIS is unknown, making student preparation for CyberGIS competency difficult. To address this gap, we reviewed the CyberGIS literature to develop a set of 37 key CyberGIS concepts. Then, we used Q‐methodology to interview 20 expert GIS users to understand the components of CyberGIS they viewed as most applicable to their GIS practice. Three distinct conceptions of CyberGIS expertise emerged: the spatial analyst, the domain‐specific problem solver, and the CyberGIS‐enabled computer scientist. The CyberGIS‐enabled computer scientist emphasized the underlying technologies which allow spatial analysts to discover and implement advanced methods and processes in GIS, which are then utilized by problem solvers to address domain‐focused issues. These three contrasting perceptions of CyberGIS expertise raise questions about the nature of curriculum and instruction appropriate to develop competent CyberGIS practitioners. With the identified variety of expert practices concerning CyberGIS, confusing practices and contrasting visions of the nature of CyberGIS reveal a need for instructional organization. [ABSTRACT FROM AUTHOR]

Geographic information systems (GIS) are fundamental information technologies. The capabilities and applications of GIS continue to rapidly expand, requiring practitioners to have new skills and competencies, especially in computer science. There is little research, however, about how best to prepare the next generation of GIScientists with adequate computer science skills. This article explores how U.S. geography departments are introducing and developing computer science and programming skills in their geography and GIS degree programs. We review the degree requirements in fifty-five geography departments and discover that forty-four of them offer some kind of GIS programming course. Of the 210 separate degree options identified, however, only 22 require one of these courses for a degree. There is little consistency or emphasis on computer science and programming skills in geography or GIS degrees, despite the immense importance of these components in geography and GIS careers. We propose future research along distinct investigative tracks to build a research-based understanding of the educational interactions among GIS, computer science, programming, and geography. [ABSTRACT FROM PUBLISHER]

Introductory courses in Geographic Information Science (GIS) expose students to the concepts and practices necessary for future academic and professional use of GIS tools. Traditional GIS courses balance lectures in the theories of GIS with pre-built and pre-packaged lab activities to learn the practices of GIS. This article presents a case study of an experimental introductory course in which students conducted novel, independent project-based group research under the supervision of graduate or advanced undergraduate students enrolled in the course, culminating in a class presentation and publication quality paper. Surveys and interviews indicated that students reacted more positively to project-based group research than to traditional activities. Students felt the projects better prepared them for 'real world' applications of GIS, and recommend project-based group research in other GIS coursework. Additionally, our findings indicate that students appreciate interactions with peers of varying skill levels and experiences, as these broaden their capabilities to work with other GIS users. [ABSTRACT FROM AUTHOR]

Background: The utility of patient attributes associated with the spatiotemporal analysis of medical records lies not just in their values but also the strength of association between them. Estimating the extent to which a hierarchy of conditional probability exists between patient attribute associations such as patient identifying fields, patient and date of diagnosis, and patient and address at diagnosis is fundamental to estimating the strength of association between patient and geocode, and patient and enumeration area. We propose a hierarchy for the attribute associations within medical records that enable spatiotemporal relationships. We also present a set of metrics that store attribute association error probability (AAEP), to estimate error probability for all attribute associations upon which certainty in a patient geocode depends. Methods: A series of experiments were undertaken to understand how error estimation could be operationalized within health data and what levels of AAEP in real data reveal themselves using these methods. Specifically, the goals of this evaluation were to (1) assess if the concept of our error assessment techniques could be implemented by a population-based cancer registry; (2) apply the techniques to real data from a large health data agency and characterize the observed levels of AAEP; and (3) demonstrate how detected AAEP might impact spatiotemporal health research. Results: We present an evaluation of AAEP metrics generated for cancer cases in a North Carolina county. We show examples of how we estimated AAEP for selected attribute associations and circumstances. We demonstrate the distribution of AAEP in our case sample across attribute associations, and demonstrate ways in which disease registry specific operations influence the prevalence of AAEP estimates for specific attribute associations. Conclusions: The effort to detect and store estimates of AAEP is worthwhile because of the increase in confidence fostered by the attribute association level approach to the assessment of uncertainty in patient geocodes, relative to existing geocoding related uncertainty metrics. [ABSTRACT FROM AUTHOR]

Background Geocoding, the process of converting textual information describing a location into one or more digital geographic representations, is a routine task performed at large organizations and government agencies across the globe. In a health context, this task is often a fundamental first step performed prior to all operations that take place in a spatially-based health study. As such, the quality of the geocoding system used within these agencies is of paramount concern to the agency (the producer) and researchers or policy-makers who wish to use these data (consumers). However, geocoding systems are continually evolving with new products coming on the market continuously. Agencies must develop and use criteria across a number axes when faced with decisions about building, buying, or maintaining any particular geocoding systems. To date, published criteria have focused on one or more aspects of geocode quality without taking a holistic view of a geocoding system's role within a large organization. The primary purpose of this study is to develop and test an evaluation framework to assist a large organization in determining which geocoding systems will meet its operational needs. Methods A geocoding platform evaluation framework is derived through an examination of prior literature on geocoding accuracy. The framework developed extends commonly used geocoding metrics to take into account the specific concerns of large organizations for which geocoding is a fundamental operational capability tightly-knit into its core mission of processing health data records. A case study is performed to evaluate the strengths and weaknesses of five geocoding platforms currently available in the Australian geospatial marketplace. Results The evaluation framework developed in this research is proven successful in differentiating between key capabilities of geocoding systems that are important in the context of a large organization with significant investments in geocoding resources. Results from the proposed methodology highlight important differences across all axes of geocoding system comparisons including spatial data output accuracy, reference data coverage, system flexibility, the potential for tight integration, and the need for specialized staff and/or development time and funding. Such results can empower decisions-makers within large organizations as they make decisions and investments in geocoding systems. [ABSTRACT FROM AUTHOR]

Address ranges used in linear interpolation geocoding often have errors and omissions that result in input address numbers falling outside of known address ranges. Geocoding systems may match these input addresses to the closest available nearby address range and assign low confidence values (match scores) to increase match rates, but little is published describing the matching or scoring techniques used in these systems. This article sheds light on these practices by investigating the need for, technical approaches to, and utility of nearby matching methods used to increase match rates in geocode data. The scope of the problem is motivated by an analysis of a commonly used health dataset. The technical approach of a geocoding system that includes a nearby matching approach is described along with a method for scoring candidates based on spatially-varying neighborhoods. This method, termed dynamic nearby reference feature scoring, identifies, scores, ranks, and returns the most probable candidate to which the input address feature belongs or is spatially near. This approach is evaluated against commercial systems to assess its effectiveness and resulting spatial accuracy. Results indicate this approach is viable for improving match rates while maintaining acceptable levels of spatial accuracy. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports published within the issue on topics including the challenges for geocoding twitter messages, geo-parsing crisis messages from microblogs, and geocoding methodology for spatio-temporal data.

Geocoding systems typically use more than one geographic reference dataset to improve match rates and spatial accuracy, resulting in multiple candidate geocodes from which the single “best” result must be selected. Little scientific evidence exists for formalizing this selection process or comparing one strategy to another, leading to the approach used in existing systems which we term the hierarchy-based criterion: place the available reference data layers into qualitative, static, and in many cases, arbitrary hierarchies and attempt a match in each layer, in order. The first non-ambiguous match with suitable confidence is selected and returned as output. This approach assumes global relationships of relative accuracy between reference data layers, ignoring local variations that could be exploited to return more precise geocodes. We propose a formalization of the selection criteria and present three alternative strategies which we term the uncertainty-, gravitationally-, and topologically-based strategies. The performance of each method is evaluated against two ground truth datasets of nationwide GPS points to determine any resulting spatial improvements. We find that any of the three new methods improves on current practice in the majority of cases. The gravitationally- and topologically-based approaches offer improvement over a simple uncertainty-based approach in cases with specific characteristics. [ABSTRACT FROM AUTHOR]

Spatial accessibility provides significant policy implications, describing the spatial disparity of access and supporting the decision-making process for placing additional infrastructure at adequate locations. Several previous reviews have covered spatial accessibility literature, focusing on empirical findings, distance decay functions, and threshold travel times. However, researchers have underexamined how spatial accessibility studies benefitted from the recently enhanced availability of dynamic variables, such as various travel times via different transportation modes and the finer temporal granularity of geospatial data in these studies. Therefore, in our review, we investigated methodological advancements in place-based accessibility measures and scrutinized two recent trends in spatial accessibility studies: multimodal spatial accessibility and temporal changes in spatial accessibility. Based on the critical review, we propose two research agendas: improving the accuracy of measurements with dynamic variable implementation and furnishing policy implications granted from the enhanced accuracy. These agendas particularly call for the action of geographers on the full implementation of dynamic variables and the strong linkage between accessibility and policymaking. [ABSTRACT FROM AUTHOR]

Road anomaly detection is essential in road maintenance and management; however, continuously monitoring road anomalies (such as bumps and potholes) with a low-cost and high-efficiency solution remains a challenging research question. In this study, we put forward an enhanced mobile sensing solution to detect road anomalies using mobile sensed data. We first create a smartphone app to detect irregular vehicle vibrations that usually imply road anomalies. Then, the mobile sensed signals are analyzed through continuous wavelet transform to identify road anomalies and estimate their sizes. Next, we innovatively utilize a spatial clustering method to group multiple driving tests' results into clusters based on their spatial density patterns. Finally, the optimized detection results are obtained by synthesizing each cluster's member points. Results demonstrate that our proposed solution can accurately detect road surface anomalies (94.44%) with a high positioning accuracy (within 3.29 meters in average) and an acceptable size estimation error (with a mean error of 14 cm). This study suggests that implementing a crowdsensing solution could substantially improve the effectiveness of traditional road monitoring systems. [ABSTRACT FROM AUTHOR]