Fig 1 - uploaded by Nadine Schuurman
Content may be subject to copyright.
Source publication
Spatial cluster analysis is a uniquely interdisciplinary endeavour, and so it is important to communicate and disseminate ideas, innovations, best practices and challenges across practitioners, applied epidemiology researchers and spatial statisticians. In this research we conducted a scoping review to systematically search peer-reviewed journal da...
Contexts in source publication
Context 1
... as both class- es of methods analyse the data at the individual level, and only differ in the scale at which they are evaluated. Methods were then further categorised based on the type of data: areal, point and line. All studies that used point features to represent individual level, address location data in their analysis were included. Fig. 1 illus- trates the process of the scoping ...
Context 2
... themes over several meetings and subsequent content was collaboratively generated. Our goal in identifying themes was to summarise overall patterns of each paper's method implementation aspects. The identifica- tion of themes was also guided by the authors expert knowledge and key review papers. A detailed descrip- tion of stages can be found in Fig. ...
Citations
... The approaches to formally assess the presence of clusters were classified according to the dimensions and data forms proposed by (17,32). A modified version of the framework proposed by (18) was used as a guide to classify the analytical tools used for spatial or spatial-temporal analysis based on their purpose, as previously described. ...
Background: Sustained Peste des petits ruminants (PPR) circulation, as evidenced by surveillance, shows PPR endemicity in Africa and Asia. Regional transmission of PPR is enabled by joining numerous epidemiological factors. Spatial, spatiotemporal and transmission dynamics analytical methods have been used to explore the risk of PPR transmission. The dearth of information on the risk factors associated with spatiotemporal distribution and transmission dynamics of PPR at a regional scale is high. Through a thorough analysis of peer-reviewed literature, this study sought to evaluate the risks of Peste des Petit ruminant virus (PPRV) epidemics by noting distinctions of geographical and spatial-temporal approaches applied in endemic settings.
Methods: A scoping literature review of PPR research publications that used spatial and spatiotemporal approaches to assess PPR risks in endemic areas was carried out using PubMed and Google Scholar data base.
Results: Out of 42 papers selected 19 focused on Asia, 15 on Africa, and 8 had a global view. 61.9% used clustering analysis while 35.7% used spatial autocorrelation. Temporal trends were described by most studies at about 71.2% while modeling approaches were used by 13 articles (30%). Five risk factors evaluated include demographics and livestock–wildlife interactions (n = 20), spatial accessibility (n = 19), trade and commerce (n = 17), environment and ecology (n = 12), and socioeconomic aspects (n=9). Transmission dynamics of PPR was covered in almost all articles except 2 articles but it has linked all the risk factors.
Conclusions: The review has contributed to the shifting and improvement of our understanding on PPR outbreaks in endemic settings and support evidence-based decision-making to mitigate the impact of the virus on small ruminant populations. Linkage of other risk factors to livestock trade which is the major driver of livestock movement has been shown to pose a significant risk of PPR epidemics in endemic settings. With many studies being found in Asia compared to Africa, future development of predictive models to evaluate possible eradication strategies at national and regional levels should also consider Africa.
... This process is an intricate, interdisciplinary system that integrates various methodologies. Among numerous methods employed in identifying geothermal anomalies are TIR remote sensing technology [11], geophysical exploration techniques [12], geochemical analytical methods [13], mathematical statistical models [14], and spatial analysis techniques [15]. TIR remote sensing technology, due to its extensive information content, high detection precision, and capability for rapid large-scale identification with minimal constraints 2 of 19 from ground conditions, offers significant advantages in terms of efficiency and costeffectiveness. ...
This paper discusses thermal infrared (TIR) remote sensing technology applied to the delineation of geothermal resources, a significant renewable energy source. The technical characteristics and current status of TIR remote sensing is discussed and related to the integration of geological structure, geophysical data, and geochemical analyses. Also discussed are surface temperature inversion algorithms used to delineate anomalous ground-surface temperatures. Unlike traditional geophysical and geochemical exploration methods, remote sensing technology exhibits considerable advantages in terms of convenience and coverage extent. The paper addresses the major challenges and issues associated with using TIR remote sensing technology in geothermal prospecting.
... It represents a combination of spatial and temporal attributes at a particular moment. While the application of this analysis in the interior environment is relatively less common compared to other scenarios like urban transport, tourism, climate environment and public health [52][53][54][55], there exists significant potential for employing this method to analyse spatial and temporal patterns of human behaviour within indoor environments, as demonstrated in this study. ...
... Unsupervised machine learning: Spatial clustering. Spatial cluster analysis is an exploratory machine learning tool for gaining a greater understanding of a dataset [52], to uncover the spatial relationships and reveal areas of concentrated or dispersed occurrences. Density-Based Spatial Clustering of Applications with Noise (DBSCN) is the unsupervised learning clustering technique that identifies the connected area with a high concentration of points, separated from other clusters with a lower point density [63]. ...
The future of workspace is significantly shaped by the advancements in technologies, changes in work patterns and workers' desire for an improved well-being. Co-working space is an alternative workspace solution, for cost-effectiveness, the opportunity for diverse and flexible design and multi-use. This study examined the human-centric design choices using spatial and temporal variation of occupancy levels and user behaviour in a flexible co-working space in London. Through a machine-learning-driven analysis, we investigated the time-dependent patterns, decompose space usage, calculate seat utilisation and identify spatial hotspots. The analysis incorporated a large dataset of sensor-detected occupancy data spanning 477 days, comprising more than 140 million (145×10 6) data points. Additionally, on-site observations of activities were recorded for 13 days spanning over a year, with 110 time instances including more than 1000 snapshots of occupants' activities, indoor environment , working behaviour and preferences. Results showed that the shared working areas positioned near windows or in more open, connected and visible locations are significantly preferred and utilised for communication and working, and semi-enclosed space on the side with less visibility and higher privacy are preferred for focused working. The flexibility of multi-use opportunity was the most preferred feature for hybrid working. The findings offer data-driven insights for human-centric space planning and design of office spaces in the future, particularly in the context of hybrid working setups, hot-desking and co-working systems.
... Street- (Fritz, et al., 2013;Musa et al., 2013). Las recomendaciones hechas por el Dr. Snow, al notificar a las autoridades sus hallazgos propuso la clausura de dicha bomba reduciendo significativamente los casos y además impulsó la política pública del entubamiento de aguas hervidas para evitar la contaminación de los pozos de agua potable. ...
En las últimas décadas, muchos expertos en educación han manifestado que gran parte de los problemas y crisis a los que se enfrenta la sociedad tienen su origen, o podrían haber sido evitados, si se prestara una mayor atención a la educación moral y del carácter de los jóvenes.
... Street- (Fritz, et al., 2013;Musa et al., 2013). Las recomendaciones hechas por el Dr. Snow, al notificar a las autoridades sus hallazgos propuso la clausura de dicha bomba reduciendo significativamente los casos y además impulsó la política pública del entubamiento de aguas hervidas para evitar la contaminación de los pozos de agua potable. ...
La educación del carácter es uno de los objetivos más importantes del proceso educativo formal en el mundo, y por ello, también en México. A lo largo del tiempo, diferentes esfuerzos se han hecho, con mayor o menor éxito y sistematización en esta materia, sin que hasta el momento exista un programa medido y evaluado en virtud de su eficacia, e implementado a mayor escala. En suma, este rubro, sigue siendo un objetivo pendiente en el sistema educativo formal.
... There are a variety of GIS methods to identify spatial clusters and relationships among several data layers. Importantly, the methodology should follow a series of steps to establish the neighborhood distances between observations that are valid for each unique study area [18][19][20][21][22][23][24][25]. Therefore, we computed both global and local spatial statistics to systematically assess the degree of spatial clustering. ...
... Cluster and Grouping Analysis identified statistically significant locations of social characteristics as related to attendance at the follow-up visit. Other studies have used similar cluster analysis techniques [18,[21][22][23][36][37][38][39] and research is being conducted to develop new methods of cluster analysis [22]. As with all studies that use GWR, it is important to review the results to identify places where the regression equations have lower results (e.g. ...
Stroke is the leading cause of major disability and the fifth leading cause of death in the United States. Stroke incidence across the U.S. is not uniform where the southeastern states, known as the “Stroke Belt”, have historically higher rates. Importantly, while the national average death rate due to stroke has been declining, the death rate in the Stroke Belt (from 2013 to 2015) increased 4.2% overall and 5.8% within the Hispanic population. Healthcare interventions have been designed to improve acute stroke care, but they are less prevalent in addressing post-acute care needs of stroke survivors. Therefore, this chapter will describe the results of a recent study that investigated patterns in post-stroke care using a sequence of geospatial statistics. Through this investigation, the reader will learn the sequence of Geographic Information System (GIS) techniques appropriate to use when studying complex spatial patterns.
... Additionally, hotspot analysis (Fritz et al., 2013) was used to statistically assess whether or not the concentration and scattered locations were the result of chance. This analysis considered the geolocation of late-stage diagnoses. ...
... Therefore, the main strength was to recognize the distribution, patterns and geographical differences according to the home address of patients in order to reevaluate probability of late BC diagnosis. In this sense, the geospatial analysis presented in this study used the most recommended spatial epidemiology techniques to identify structures, trends, and patterns underlying within a given data set (Auchincloss et al., 2012, Fritz et al., 2013Wang et al., 2015;Auchincloss et al., 2012). Furthermore, the methodology of this study is reproducible and as more georeferenced data is available, it could be used not only in a different or more recent study period, but also in another territorial scale (census unit, neighborhood, municipality, region, state) or context, as well as other important clinical variables, that is, BI-RADS categories, histological grade and variety, hormone receptors, to name a few. ...
Objective:
The aim of this study was to show how a geospatial model can be used to identify areas with a higher probability for late-stage breast cancer (BC) diagnoses.
Methods:
Our study considered an ecological design. Clinical records at a tertiary care hospital were reviewed in order to obtain the place of residence and stage of the disease, which was classified as early (0-IIA) and late (IIB-IV) and whose diagnoses were made during the 2013-2017 period. Then, they were geolocated to identify the distribution and spatial trend. Subsequently, the pattern of location, i.e. scattered, random and concentrated, was statistically assessed and a geospatial model was elaborated to determine the probability of late diagnoses in the state of Jalisco, Mexico.
Result:
There were 1 954 (N) geolocated BC diagnoses: 58.3% were late. During the five-year period, a southwest-northeast trend was identified, nearly 9.5% of the surface of Jalisco, where 6 out of 10 (n= 751) late- stage diagnoses were concentrated. A concentrated and statistically significant pattern was identified in the southern, central and northern Pacific area of Jalisco, where the geospatial model delimited the places with the highest probability of late clinical stages (p <0.05).
Conclusion:
The geographical differences associated with the late diagnoses of BC suggest it is necessary to adapt and focus the strategies for early detection as an alternative to create a major impact on the population. Reproducible analysis tools were used in other contexts where geolocation data are available to complement public policies and strategies aimed to control BC.
... Additionally, hotspot analysis (Fritz et al., 2013) was used to statistically assess whether or not the concentration and scattered locations were the result of chance. This analysis considered the geolocation of late-stage diagnoses. ...
... Therefore, the main strength was to recognize the distribution, patterns and geographical differences according to the home address of patients in order to reevaluate probability of late BC diagnosis. In this sense, the geospatial analysis presented in this study used the most recommended spatial epidemiology techniques to identify structures, trends, and patterns underlying within a given data set (Auchincloss et al., 2012, Fritz et al., 2013Wang et al., 2015;Auchincloss et al., 2012). Furthermore, the methodology of this study is reproducible and as more georeferenced data is available, it could be used not only in a different or more recent study period, but also in another territorial scale (census unit, neighborhood, municipality, region, state) or context, as well as other important clinical variables, that is, BI-RADS categories, histological grade and variety, hormone receptors, to name a few. ...
Objective: The aim of this study was to show how a geospatial model can be used to identify areas with a higher probability for late-stage breast cancer (BC) diagnoses. Methods: Our study considered an ecological design. Clinical records at a tertiary care hospital were reviewed in order to obtain the place of residence and stage of the disease, which was classified as early (0-IIA) and late (IIB-IV) and whose diagnoses were made during the 2013-2017 period. Then, they were geolocated to identify the distribution and spatial trend. Subsequently, the pattern of location, i.e. scattered, random and concentrated, was statistically assessed and a geospatial model was elaborated to determine the probability of late diagnoses in the state of Jalisco, Mexico. Result: There were 1 954 (N) geolocated BC diagnoses: 58.3% were late. During the five-year period, a southwest-northeast trend was identified, nearly 9.5% of the surface of Jalisco, where 6 out of 10 (n= 751) late-stage diagnoses were concentrated. A concentrated and statistically significant pattern was identified in the southern, central and northern Pacific area of Jalisco, where the geospatial model delimited the places with the highest probability of late clinical stages (p <0.05). Conclusion: The geographical differences associated with the late diagnoses of BC suggest it is necessary to adapt and focus the strategies for early detection as an alternative to create a major impact on the population. Reproducible analysis tools were used in other contexts where geolocation data are available to complement public policies and strategies aimed to control BC. 3
... Spatial clustering solutions are evaluated with statistical [121] (e.g., likelihood ratio, autocorrelation, significance) and similarity [54] (e.g., euclidean distance, pearson correlation) metrics. Spatial-clustering challenges include irregularly shaped clusters, high dimensional data, spatial relations/weights selection, resolution, object interactions, and visual vs. quantitative evaluation [113,122,123]. ...
Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine-learning models in the industry without requiring abundant expert knowledge and resources. This paper reviews recent literature from 136 papers, and proposes a general framework for integrating spatial decision support systems with automated machine learning as an opportunity to lower major user adoption barriers. Challenges of data quality, model interpretability, and practical usefulness are discussed as general considerations for system implementation. Research opportunities related to spatially explicit models in AutoML, and resource-aware, collaborative/connected, and human-centered systems are also discussed to address these challenges. This paper argues that integrating automated machine learning into spatial decision support systems can not only potentially encourage user adoption, but also mutually benefit research in both fields—bridging human-related and technical advancements for fostering future developments in spatial decision support systems and automated machine learning.
... 49 Local cluster statistics such as Anselin's local Moran's I quantify spatial autocorrelation and clustering at the small area level. 63 As this research aimed to detect both spatial variation and spatial clusters, we applied both the global and local methods. Numerous spatial variations have been identified in NILRD incidence in the study area with some significant HH and LL clusters. ...
Background and Aims
Geography plays an important role in the incidence of respiratory diseases. The aim of this study was to investigate the epidemiology and geographical distribution of death due to noninfectious lower respiratory diseases (NILRDs).
Methods
Data related to all death due to NILRD in Kerman Province between 2012 and 2018 were extracted from the National Mortality Registry. The underlying causes of death were extracted from the registry based on the assigned codes from ICD‐10 (International Classification of Diseases 10th Revision) classification. The existence of spatial clusters and outliers was evaluated using local indicators of spatial association statistics.
Results
The frequency of death due to NILRD was 8005 persons during the 7 years of the study. The main cause of death was chronic lower respiratory disease (54.2%). Other causes of death were, respectively, lung diseases due to external agents (1.09%), other respiratory diseases mainly affecting the interstitium (1.16%), other diseases of pleura (0.57%), and other diseases of the respiratory system (42.13%). The age‐ and sex‐adjusted mortality rates due to NILRD in the north and center of the province increased significantly from 2012 to 2018. Also, the results of cluster analysis identified northern regions as the clustered areas of NILRD.
Conclusions
Our findings showed a significant increase in mortality due to NILRD in Kerman Province during the 7 years of the study. To reduce this type of death, health policymakers should have environmental health plans and basic solutions, such as a warning system to reduce the commuting on highly air‐polluted days and to control pollutants, especially in the industrial areas of the north of this province.
