Mapping Platform Research Articles

Digitalization of Micro, Small and Medium Enterprises in Nagari Muaro Pingai, Junjung Sirih District, Solok Regency, West Sumatra

This program aims to introduce the benefits of science and technology to Micro, Small, and Medium Enterprises (MSMEs) in Nagari Muaro Pingai due to the limited market share and need for more awareness of products produced by these MSMEs. Through this digitalization work program, the community service team from Universitas Andalas assisted five MSME entrepreneurs by strengthening their business branding, providing financial assistance, and digital promotion. The methods employed include direct data collection, interview surveys, and providing training directly to the community. The financial assistance included mobile phones to help them digitize their businesses. The outcomes of those MSME's digitalization work programs included business logos, promotional brochures, and business location registration on the Google Maps platform. The community engagement activities further expanded market reach, increased income, and simplified transaction processes for MSME entrepreneurs in Nagari Muaro Pingai.

Temporal Dynamics of Citizen-Reported Urban Challenges: A Comprehensive Time Series Analysis

Temporal Dynamics of Citizen-Reported Urban Challenges: A Comprehensive Time Series Analysis

Socio-spatial equity analysis of relative wealth index and emergency obstetric care accessibility in urban Nigeria

BackgroundBetter geographical accessibility to comprehensive emergency obstetric care (CEmOC) facilities can significantly improve pregnancy outcomes. However, with other factors, such as affordability critical for care access, it is important to explore accessibility across groups. We assessed CEmOC geographical accessibility by wealth status in the 15 most-populated Nigerian cities.MethodsWe mapped city boundaries, verified and geocoded functional CEmOC facilities, and assembled population distribution for women of childbearing age and Meta’s Relative Wealth Index (RWI). We used the Google Maps Platform’s internal Directions Application Programming Interface to obtain driving times to public and private facilities. City-level median travel time (MTT) and number of CEmOC facilities reachable within 60 min were summarised for peak and non-peak hours per wealth quintile. The correlation between RWI and MTT to the nearest public CEmOC was calculated.ResultsWe show that MTT to the nearest public CEmOC facility is lowest in the wealthiest 20% in all cities, with the largest difference in MTT between the wealthiest 20% and least wealthy 20% seen in Onitsha (26 vs 81 min) and the smallest in Warri (20 vs 30 min). Similarly, the average number of public CEmOC facilities reachable within 60 min varies (11 among the wealthiest 20% and six among the least wealthy in Kano). In five cities, zero facilities are reachable under 60 min for the least wealthy 20%. Those who live in the suburbs particularly have poor accessibility to CEmOC facilities.ConclusionsOur findings show that the least wealthy mostly have poor accessibility to care. Interventions addressing CEmOC geographical accessibility targeting poor people are needed to address inequities in urban settings.

About the published content on each section

About the published content on each section

Citizen empowerment as a police force multiplier: Reproducing social domination through a 21st century personal safety app

Citizen is a digital mapping platform and personal safety app that boasts over 10 million users in the United States. Through the platform, users can report crimes, map safe routes, or rely on the app’s other functions to protect themselves from dangerous situations. Sold on a promise of empowerment, Citizen markets itself as a 21st century technology capable of repairing the ills of our social world. In this article we analyze how Citizen taps into the desire for control and safety and urges its users to actively protect their own communities. As such, we suggest that while surveillant in nature, Citizen revolves around the force inherent to police power, transforming its users via police power by first integrating them into their social platform. Ultimately, Citizen reminds us that police power is not limited to ideological or violent exchanges, but can be a compelling solution to community problems. Rather than a progressive fix to the issues between policing and communities, however, we find that Citizen offers an expansion of police power at the individual scale, reproducing social domination both at the level of capital and the state.

Enhancing Urban Resilience: Smart City Data Analyses, Forecasts, and Digital Twin Techniques at the Neighborhood Level

Smart cities, leveraging advanced data analytics, predictive models, and digital twin techniques, offer a transformative model for sustainable urban development. Predictive analytics is critical to proactive planning, enabling cities to adapt to evolving challenges. Concurrently, digital twin techniques provide a virtual replica of the urban environment, fostering real-time monitoring, simulation, and analysis of urban systems. This study underscores the significance of real-time monitoring, simulation, and analysis of urban systems to support test scenarios that identify bottlenecks and enhance smart city efficiency. This paper delves into the crucial roles of citizen report analytics, prediction, and digital twin technologies at the neighborhood level. The study integrates extract, transform, load (ETL) processes, artificial intelligence (AI) techniques, and a digital twin methodology to process and interpret urban data streams derived from citizen interactions with the city’s coordinate-based problem mapping platform. Using an interactive GeoDataFrame within the digital twin methodology, dynamic entities facilitate simulations based on various scenarios, allowing users to visualize, analyze, and predict the response of the urban system at the neighborhood level. This approach reveals antecedent and predictive patterns, trends, and correlations at the physical level of each city area, leading to improvements in urban functionality, resilience, and resident quality of life.

Unveiling the impact of machine learning algorithms on the quality of online geocoding services: a case study using COVID-19 data

AbstractIn today's era, the address plays a crucial role as one of the key components that enable mobility in daily life. Address data are used by global map platforms and location-based services to pinpoint a geographically referenced location. Geocoding provided by online platforms is useful in the spatial tracking of reported cases and controls in the spatial analysis of infectious illnesses such as COVID-19. The first and most critical phase in the geocoding process is address matching. However, due to typographical errors, variations in abbreviations used, and incomplete or malformed addresses, the matching can seldom be performed with 100% accuracy. The purpose of this research is to examine the capabilities of machine learning classifiers that can be used to measure the consistency of address matching results produced by online geocoding services and to identify the best performing classifier. The performance of the seven machine learning classifiers was compared using several text similarity measures, which assess the match scores between the input address data and the services' output. The data utilized in the testing came from four distinct online geocoding services applied to 925 addresses in Türkiye. The findings from this study revealed that the Random Forest machine learning classifier was the most accurate in the address matching procedure. While the results of this study hold true for similar datasets in Türkiye, additional research is required to determine whether they apply to data in other countries.

Enhancing Spatial Thinking Awareness of World-Scale Geography with Excel Dynamic Map Charts and Virtual Globes

Spatial thinking is a unique thinking skill that geographers use to reason. Every individual is believed to have this thinking skill, but not all are aware of it. This condition causes differences in the development of each person. A person’s spatial thinking can be improved by training. Geospatial technology is a representation tool that many people believe can be used to train spatial thinking skills. However, not many people encounter obstacles when using this technology. The complexity of the command to run is an obstacle that is often found. In line with the development of geospatial technology, many applications integrate this technology as part of visualization tools. This teaching and learning were conducted with Action Research Classroom (three cycles) in the form of Project-Based Learning with Science, Engineering, Technology, and Mathematic (STEM) approach. This article discusses the findings of the research on the use of Excel dynamic map chart and virtual globe to improve spatial thinking in research subjects with the case study of Indonesian Geography and World Regional Geography. With the Excel application project, for the context of upper-level education, the findings show an increase in spatial thinking skills and mastery of the use of mapping platforms without the need for prior experience of coding, software, or cartography, although it needs to be corroborated by other studies.

Development and Impact of a Mobile Application that Allows Users to Track Their Location on an Educational Institution Campus

This research study aims to solve user location issues within the campus at an educational institution. As this campus comprises a large number of places and departments, users often get confused about how to reach a specific location. To address this problem, the “Ubícate” (“locate by yourself” in Spanish) application was developed following the CDIO methodology, which encompasses four creative process steps: conceive, design, implement, and operate. The “Ubícate” app provides users with information on places of interest such as schools, departments, halls, auditoriums, and sports venues, offering a visual reference of available locations through 360-degree images. The application also uses Google Maps to track user location within the campus, thus marking a reference route between university gates and the different locations available, in addition to providing information on university-sponsored events. In this paper, Section 2 describes the methodology and each of the stages that were addressed in the following sections. Section 3 presents the development itself and the data used for the purposes thereof. Next, Section 4 reveals the results from this study. Later, Section 5 assesses these results and the findings from the study. In Section 6, our conclusions are discussed. Finally, Section 7 lists topics for future research. The application did indeed contribute to improving the attendance of the academic community at events. Where the application was used, the first-hand perception of visitors and their own was very positive and enhanced the institutional image and sense of belonging. The contribution of this study consists of presenting a mobile application as a solution from three approaches: the technical aspects for application development, the business vision to satisfy the user’s needs, and the end user’s perception. All three approaches provide a technical reader, an entrepreneur, or an end user an overview of a scalable solution to different types of implementations in different types of businesses that require indoor location through the use of technologies in mobile applications. The mobile application performs the location indoors using the Google Maps platform, allowing a more agile development in implementing the APP.

Prediction of road traffic noise in the vicinity of trunk roads in Japan using digital road map platform

Prediction of road traffic noise in the vicinity of trunk roads in Japan using digital road map platform

CLIM-SEG: A generalizable segmentation model for heat and flood risk mapping

With the escalating impact of climate change coupled with increased urbanization, many cities will experience extreme heat events and intense flooding. Current modeling approaches often fail to incorporate high-resolution, frequently updated data sources, such as aerial imagery from web mapping platforms, limiting their effectiveness in identifying areas at risk. To address this gap, the paper presents CLIM-SEG, a novel framework for high-resolution urban heat and flood risk assessment, addressing critical gaps in current climate risk modeling. This framework integrates semantic segmentation of aerial imagery with a weighted sum approach that integrates environmental, socioeconomic, and building data to provide comprehensive risk evaluations at the census tract level. CLIM-SEG synthesize land cover data with hazard and vulnerability factors, producing risk scores ranging from 0 to 1. This low-cost and efficient framework can enable urban planners to prioritize resources for flood mitigation and heat adaptation, addressing the limitations of current approaches and contributing to the field of urban planning and climate change adaptation. The propoosed methodology incorporates a custom-curated dataset of 545 aerial images, including 145 manually annotated segmentation maps, to fine-tune advanced semantic segmentation models. The optimized Segmenter model achieves a pixel accuracy of 97.85% and an Intersection over Union (IoU) of 0.9578 for key urban features, significantly outperforming baseline models. Boston is selected to represent an ideal representation for both heat and flood risk, as the city experiences severe urban heat islands, and is susceptible to coastal and riverine flooding, with over 11,000 structures expected to be affected by 2070 due to sea level rise and increased precipitation. Results from flood and heat risk models indicate that census tracts in South End have the highest flood risk, with a weighted score value of 0.825, while census tracts in the Fenway-Kenmore neighborhood show the highest heat risk, with a score of 0.991. Both of these results have also been verified with other heat and flood risk mapping sources for Boston. The proposed framework of CLIM-SEG not only addresses the challenges faced by Boston but also has the potential to be scaled to other urban areas dealing with the impacts of climate change, providing a valuable tool for risk assessment and decision-making in the face of a changing climate.

Utilizing MapBox API, Java, and ICT in the creation of agricultural interactive maps for improved farm management and decision-making

<abstract> <p>Agriculture's sustainability is a subject of concern, and digital farming technology has been proposed to solve this problem. The digital revolution is transforming agriculture by utilizing modern equipment, computerized tools, and information and communication technology (ICT) to improve decision-making and productivity. Digital farming technology enables even inexperienced farmers to apply such techniques by using the IoT and AI to collect and analyze data from their farming practices and the surrounding environment to improve productivity. The versatile mapping platform Mapbox is utilized to construct the map, which allows for custom data layer integration and interactive features. Backend development is performed using the Java programming language, which facilitates seamless data processing, storage, and retrieval. The interactive map allows for dynamic overlays of crucial information, such as plot numbers, measurements, crop details, crop health assessments, NDVI, RGB, and DEM. The study involved data collection, analysis of the data, and thematic layer development using GIS to create interactive maps. In this research, two sets of DJI drones, Agisoft Metashape software, QGIS, and Mapbox were used to collect and prepare the data for the interactive map. The data was used to create the results, which were web maps that had several interactive features, such as "display popup on hover, " "swipe between maps, " and "change a map's style." The result was a thematic layer of information such as RGB, NDVI, DEM, and other field information. This research demonstrated the benefits and applicability of information technology for digital transformation in agriculture under the DX Project launched at Niigata University, Japan. This aids in the goal of producing interactive agricultural maps based on map classification, content element analysis, the development of GIS capabilities, and remote sensing data.</p> </abstract>

PERILS OF PLACE: GEOFENCES AND PREDATORY PLATFORM INTIMACIES

For many, mapping platforms are enmeshed in everyday experiences. We navigate, locate, and move through the world with the help of their locative affordances. Consequently, these platforms have an intimate awareness of our movements and location history, and this information is valuable for advertisers. One way that platforms can track and share this information is through geofences, commonly used by companies to send targeted advertisements directly to platforms. Geofences are virtual perimeters established around target locations that act as a digital tripwire, marking who and what crosses its threshold. Digital mapping platforms like Google Maps broker this location data to third-parties (Bui, Chang, & McIlwain, 2022). This paper examines two applications of geofences as intermediaries of locational data. The first is the use of geofences by the property platform, CIVVL, that applies geofences to facilitate and accelerate the tenant eviction process. The second is Hawk Analytics, a locational data broker that geofences abortion clinics and sells the locational data from the clinic’s clients to anti-choice organizations, in jurisdictions of the United States where such healthcare is illegal. In our analysis of locational data, we apply the concept of platform intimacies (Rambukkana and de Verteuil, 2021; Ley, & Rambukkana, 2021) to understand the techniques through which geofences access private locational details. This paper examines the spatial relations the geofence enforces and how this often-unregulated informational infrastructure can be applied to weaponize location data. We argue that the geofence enables an extractive relationship with intimate platform knowledge while it enforces hegemonic notions of trespass and belonging.

An Investigation of the Impact of Amenities on Condominium Apartment Prices in Colombo, Sri Lanka with Machine Learning

Condominium prices of a location are heavily impacted by the availability of neighbourhood amenities, which attract people to that location. Studies that employ machine learning to evaluate the impacts of amenities on condominium prices are a few, despite indications of increased accuracy. Hedonic models are used in the majority of current studies on housing pricing. Yet, the application of machine learning will improve accuracy and be more effective for the identification of multi-collinearity and non-linear relationships between property prices and the availability of amenities for a wide range of parameters. The aim of this study is to investigate the possible relationship between urban amenities surrounding condominium apartments and their market prices. The study uses data from the Google Maps Platform to examine the link between neighbourhood amenities and the prices of 236 condominiums in Colombo, Sri Lanka. 56 significant amenity characteristics were found using the eXtreme gradient boosting (XGB) algorithm, and the variety of correlations between amenities and condominium prices as restricted positive, accelerated positive, crooked, humped, and negative was explained. Results demonstrated that while a beautiful urban environment requires a variety of facilities, the popularity and other attributes of amenities affect condominium prices in numerous non-linear ways. Therefore, public and private organizations must work together to create integrative strategies that enhance and maintain the variety and accessibility of urban amenities.

Detecting, mapping and digitising canopy geometry, fruit number and peel colour in pear trees with different architecture

Increasing farm labour and input costs and the requirement for more orchard data are leading to rapid advances in technology to improve management systems in fruit production. The study aimed to (i) validate a sensorised platform to estimate fruit number, peel colour and blush coverage in pear orchards with different pear selections and tree architectures, (ii) establish relationships between fruit number, peel colour and canopy geometry features, and (iii) evaluate the platform for mapping and digitising orchard features. The study was carried out over two years in an experimental ‘ANP-0131′ orchard and in one year in two commercial pear orchards (‘ANP-0131′ and ‘PremP009’). Predictions of fruit number and blush coverage were compared in traditional three-dimensional (3D) and modern high-density two-dimensional (2D) training systems. Overall, prediction errors for fruit number were < 6.5 % in all the training systems, but improved performance was achieved in vertical 2D configurations (% standard errors = 2.2 %). Fruit number and blush coverage per unit of leaf area were higher in 2D compared to 3D training systems. Blush coverage predictions in ‘ANP-0131′ were reliable (R2 = 0.67, RMSE = 3.70 %). Accurate predictions of blush coverage classes were achieved by modifying sample variance. Fruit number and blush coverage were negatively affected by increasing canopy size. The platform proved useful for mapping and digitisation. Spatial heatmaps of orchard features provided a valuable visual aid to identify zones for priority interventions for peel colour enhancement.

A Numerical Output Technique for Voxler’s ObliqueImages and Its Application

In geophysical surveys, there will be no measured data at some location where it is inaccessible for placement of survey points or lines. The interpolation data of those locations can be obtained and displayed by using three-dimensional (3D) mapping platform. Based on the study of the file format of ObliqueImage output in Voxler platform, this paper presents a technique for extracting spatial coordinates and physical parameter values of each point in the ObliqueImage. This numerical output technique is used to map the profiled slice across the tunnel axis where boreholes can not be arranged in the electromagnetic wave CT investigation section in a dedicated karst survey of an underwater tunnel in Wuhan, rendering the karst development on the profile across the tunnel axis; after further extension, the point cloud data of bedrock surface and karst caves for building 3D geological model are extracted from isosurface files, which provides data basis for tunnel design and construction digitization.

Genetic variants underlying the neuro‐anatomical signatures of hypertension measured on structural MRI

AbstractBackgroundHypertension (HTN) is associated with gray matter (GM) atrophy and increased white matter hyperintensity (WMH) burden, increasing their susceptibility to Alzheimer’s disease and related dementias. We developed a machine learning based model to quantify spatial patterns of abnormality recognizing HTN‐related brain changes (SPARE‐HTN) from structural magnetic resonance images (sMRI). We performed a genome wide association study (GWAS) to identify the genetic variants and associated biological traits that underlie the “expression” of HTN‐related brain changes, i.e., the individualized SPARE‐HTN index.MethodSPARE‐HTN model was trained on the large multi‐study iSTAGING dataset, with regional GM volumes from T1‐weighted images and lobar WMH volumes from T2‐weighted FLAIR images used as imaging features. GWAS was carried out in a subset of N = 7490 (46% Female, Age = 65.5±7 years) hypertensive participants from UK‐Biobank. A GWAS of SPARE‐HTN scores was run using linear regression with Plink, correcting for the top 40 genetic principal components, age, sex, and intracranial volume. Significant SNPs were identified and mapped to functional genes using the Functional Mapping and Annotation (FUMA) platform; and candidate SNPs were mapped to the GWAS catalog.ResultSPARE‐HTN was associated with regional atrophy most prominent in perisylvian and temporal cortex and increased WMH volumes, particularly in frontal white matter. SPARE‐HTN scores showed better discriminative power between normal and stage 1 participants (Cohen’s‐d effect size, d = 0.2) and stage 2 (d = 0.71) hypertensive participants when compared to total WMH volume (d = 0.04 and 0.38, respectively). GWAS identified N = 321 independently significant SNPs, of which a subset N = 46 candidate SNPs were mapped to 6 genes (ICA1L, CARF, WDR12, NBEAL1, KRT8P15, CYP20A1) in Chromosome 2 (Figure 2). These genes were previously reported to be associated with several cardiovascular conditions including coronary artery disease and stroke, and with increased WM damage on sMRI and diffusion MRI (Figure 3).ConclusionThe individualized imaging marker, SPARE‐HTN, was associated with genes relevant to HTN with traits including cardiovascular conditions and higher prevalence of WMH, and was more sensitive to HTN than total WMH volume. SPARE‐HTN is a useful tool to characterize HTN‐related brain changes and monitor treatment effects at an individual level.

Genetic variants underlying the neuro‐anatomical signatures of hypertension measured on structural MRI

AbstractBackgroundHypertension (HTN) is associated with gray matter (GM) atrophy and increased white matter hyperintensity (WMH) burden, increasing their susceptibility to Alzheimer’s disease and related dementias. We developed a machine learning based model to quantify spatial patterns of abnormality recognizing HTN‐related brain changes (SPARE‐HTN) from structural magnetic resonance images (sMRI). We performed a genome wide association study (GWAS) to identify the genetic variants and associated biological traits that underlie the “expression” of HTN‐related brain changes, i.e., the individualized SPARE‐HTN index.MethodSPARE‐HTN model was trained on the large multi‐study iSTAGING dataset, with regional GM volumes from T1‐weighted images and lobar WMH volumes from T2‐weighted FLAIR images used as imaging features. GWAS was carried out in a subset of N = 7490 (46% Female, Age = 65.5±7 years) hypertensive participants from UK‐Biobank. A GWAS of SPARE‐HTN scores was run using linear regression with Plink, correcting for the top 40 genetic principal components, age, sex, and intracranial volume. Significant SNPs were identified and mapped to functional genes using the Functional Mapping and Annotation (FUMA) platform; and candidate SNPs were mapped to the GWAS catalog.ResultSPARE‐HTN was associated with regional atrophy most prominent in perisylvian and temporal cortex and increased WMH volumes, particularly in frontal white matter. SPARE‐HTN scores showed better discriminative power between normal and stage 1 participants (Cohen’s‐d effect size, d = 0.2) and stage 2 (d = 0.71) hypertensive participants when compared to total WMH volume (d = 0.04 and 0.38, respectively). GWAS identified N = 321 independently significant SNPs, of which a subset N = 46 candidate SNPs were mapped to 6 genes (ICA1L, CARF, WDR12, NBEAL1, KRT8P15, CYP20A1) in Chromosome 2 (Figure 2). These genes were previously reported to be associated with several cardiovascular conditions including coronary artery disease and stroke, and with increased WM damage on sMRI and diffusion MRI (Figure 3).ConclusionThe individualized imaging marker, SPARE‐HTN, was associated with genes relevant to HTN with traits including cardiovascular conditions and higher prevalence of WMH, and was more sensitive to HTN than total WMH volume. SPARE‐HTN is a useful tool to characterize HTN‐related brain changes and monitor treatment effects at an individual level.

Local food experiences before and after COVID-19: a sentiment analysis of EWOM

Purpose – To use Natural Language Processing (NLP) to explore how people feel and what they share online about their experiences with food. In addition, to learn how these experiences have evolved recently, differences before and during the crisis COVID -19 will be explored. Methodology/Design/Approach – A total of 35,001 reviews of restaurants and local cuisine establishments near tourist attractions in the city of Ayutthaya, Thailand, were extracted from the Google Local Guide platform. Several NLP techniques were used to analyse the text data, including sentiment analysis, word cloud analysis, and the N-gramme model. Findings – The results reveal travellers’ hidden sentiments toward dining experiences. Key attributes of experience sharing related to food activities in online reviews were identified both before and after COVID -19. From a theoretical perspective, the findings are relevant for researchers to recognise tourists’ behaviour in sharing local food experiences. From a practical perspective, decision makers will have a better understanding of tourist behaviour to develop and implement appropriate strategies. Originality of the research – This study is the first to analyse and interpret online reviews on Google Maps platform by applying text mining and sentiment analysis in gastronomic tourism research, especially in the context of COVID -19.

Molecular Diagnosis of Facioscapulohumeral Muscular Dystrophy in Patients Clinically Suspected of FSHD Using Optical Genome Mapping.

Facioscapulohumeral muscular dystrophy (FSHD) represents the third most common muscular dystrophy in the general population and is characterized by progressive and often asymmetric muscle weakness of the face, upper extremities, arms, lower leg, and hip girdle. In FSHD type 1, contraction of the number of D4Z4 repeats to 1-10 on the chromosome 4-permissive allele (4qA) results in abnormal epigenetic derepression of the DUX4 gene in skeletal muscle. In FSHD type 2, epigenetic derepression of the DUX4 gene on the permissive allele (4qA) with normal-sized D4Z4 repeats (mostly 8-20) is caused by heterozygous pathogenic variants in chromatin modifier genes such as SMCHD1, DNMT3B, or LRIF1. We present validation of the optical genome mapping (OGM) platform for accurate mapping of the D4Z4 repeat size, followed by diagnostic testing of 547 cases with a suspected clinical diagnosis of FSHD and next-generation sequencing (NGS) of the SMCHD1 gene to identify cases with FSHD2. OGM with Bionano Genomics Saphyr and EnFocus FSHD analysis software was used to identify FSHD haplotypes and D4Z4 repeat number and compared with the gold standard of Southern blot-based diagnosis. A custom Agilent SureSelect enrichment kit was used to enrich SMCHD1, followed by NGS on an Illumina system with 100-bp paired-end reads. Copy number variants were assessed using NxClinical software. We performed OGM for the diagnosis of FSHD in 547 patients suspected of FSHD between December 2019 and December 2022, including 301 male (55%) and 246 female patients (45%). Overall, 308 of the referred patients were positive for D4Z4 contraction on a permissive haplotype, resulting in a diagnosis of FSHD1. A total of 252 of 547 patients were referred for concurrent testing for FSHD1 and FSHD2. This resulted in the identification of FSHD2 in 9/252 (3.6%) patients. In our FSHD2 cohort, the 4qA allele size ranged from 8 to 18 repeats. Among FSHD1-positive cases, 2 patients had biallelic contraction and 4 patients had homozygous contraction and showed early onset of clinical features. Nine of the 308 patients (3%) positive for 4qA contraction had mosaic 4q alleles with contraction on at least one 4qA allele. The overall diagnostic yield in our cohort was 58%. A combination of OGM to identify the FSHD haplotype and D4Z4 repeat number and NGS to identify sequence and copy number variants in the SMCHD1 gene is a practical and cost-effective option with increased precision for accurate diagnosis of FSHD types 1 and 2.