Geographic Information System Data Research Articles

An analysis of qualifications and requirements for geographic information systems (GIS) positions in the United States

AbstractAs the demand for geospatial analytics continues to grow, geographic information systems (GIS) professionals are needed to build, operate, and maintain GIS technologies, data, and software to provide geospatial insights for modern industries and organizations. To best train the next generation of GIS professionals, an understanding of qualifications and requirements of GIS positions is needed. Thus, this work analyzes 508 GIS positions, grouped by position type (analysts, developers, educators, managers, specialists, technicians) to provide insights on key pre‐requisite requirements, such as education, experience, certifications, soft communication skills, programming skills, and knowledge of GIS or IT. In general, possession of a bachelor's degree in GIS, geography, or computer science, prior professional experience, and knowledge of GIS and IT software were common pre‐requisites for most GIS roles. Soft communication skills were also frequently desired for GIS roles. We also found that some position requirements tended to vary by position type, such as manager and developer roles requiring on average 5 years or higher prior experience, while analyst, specialist, and technician roles had much lower experience and education requirements. Higher education institutions and GIS training programs should note the desired requirements for GIS position types and continue to refine programs and develop pathways for success for aspiring GIS professionals.

Geographic information systems and big data analytics: optimizing digital adoption to develop tourism in Buton Islands, Indonesia

This research aims to optimize tourism potential in the Buton Islands, Indonesia, using Geographic Information Systems (GIS) and Big Data Analytics. The study addresses the low tourism potential in the region and constraints in tourism development, such as lack of information and data. Qualitative methods include document analysis, literature studies, and focus group discussions. Quantitative methods involve surveying respondents about the use of GIS and Big Data Analytics in tourism development. Data was collected from government documents, online news, and direct observation of tourist sites. The results emphasize the importance of IT and GIS in developing the tourism sector in the Buton Islands, particularly in Baubau City and South Buton Regency. GIS is crucial for identifying and managing tourism potential, while Big Data Analytics helps optimize development by analyzing visitation trends, tourist preferences, and consumer behavior patterns. The integration of these technologies can improve tourist experience and efficiency of tourism-related public services. By integrating GIS, Big Data Analytics, and Information Technology, local government and stakeholders can collaborate to create a superior tourism experience, support local economic growth, and ensure the sustainability of the natural and cultural environment in the Buton Islands.

Groundwater Assessment for Sustainable Development in the Wadi Al-Hamd Basin, Al-Madinah Al-Munawarah, KSA

This study relied on well monitoring many drilled wells data in the Wadi Al-Hamd Basin to determine water level and chemical properties from field measurements. The characteristics of groundwater must be assessed in terms of present and future eases and stresses to ensure sustainable development. Hydrogeological, hydrochemical, groundwater quality indices and Geographic Information Systems (GIS) data sets have been integrated to assess the groundwater aquifer in the study area. To implement this work, we measured the depth to the water table from eighty-eight drilled wells. Also, the major ions were determined from 82 water samples to evaluate the physicochemical properties, groundwater facies, and controlling mechanisms using diagrams such as the modified Piper proposed by Chadha, Total Ionic Salinity (TIS), and Gibbs. In this study, five irrigation water quality indicators (IWQI), including total dissolved solids (TDS), sodium adsorption ratio (SAR), potential salinity (PS), magnesium hazard (MH), and residual sodium carbonate (RSC) have been determined to assess the suitability of groundwater for irrigation. Based on the findings of this study, groundwater flows from the southeast and northwest to the west and south, respectively. In this basin, 56% of water samples have TDS greater than 2000 mg/L, while 62% of water points are categorized as Na-Cl water type, which indicates that evaporation and rock–groundwater interaction influenced groundwater quality evolution. Almost 87% of samples were rated good to excellent for irrigation use with respect to SAR. On the other hand, all water points are suitable for irrigation based on RSC. Likewise, groundwater is suitable for irrigation purposes despite the relatively high PS levels (>5 meq/L in about 49% of samples) and some small spots with high levels of MH (>50% in about 12% of samples), especially in the southern and central portions of the studied basin.

Advancing toward a sustainable future in subtropical semi-arid type climatic zone: Iraq case - The progress of solar photovoltaic energy implementation

The study delves into Iraq's shift towards sustainable energy, focusing on solar photovoltaic energy adoption and expansion to meet rising energy demands and the need for cleaner energy solutions. It highlights the potential of harnessing solar energy, particularly through small-scale solar PV systems, supported by incentives like net metering programs that encourage surplus energy feed-in to the grid. Using Geographic Information System data, the paper examines the spatial distribution of solar PV installations and projects the capacities of rooftop solar PV systems by 2035 under various promising scenarios. It also demonstrates the link between increased solar PV capacity and meeting national energy demands, underscoring the importance of strategic policies, technological advancements, and the engagement of consumers and businesses in driving Iraq towards a sustainable energy future. By fostering an environment conducive to solar PV adoption, the results offers valuable insights for ongoing discussions and future studies on renewable energy strategies in Iraq.

Social cohesion and associations with sedentary time, physical activity and dietary quality in The Maastricht Study

IntroWe aim to investigate the relationship between social cohesion and sedentary behavior (SB), total physical activity (PA), moderate-to-vigorous PA (MVPA), and dietary quality. Additionally, we assess whether these associations are independent of neighborhood walkability and the food environment. MethodsA total of 7641 participants from The Maastricht Study in the Netherlands between the ages of 40 and 75 years were analyzed. Neighborhood social cohesion was obtained by participant questionnaire completed at baseline and measured by the Dutch Livability meter. Home addresses were linked to geographic information system (GIS) data from the Geoscience and Health Cohort Consortium (GECCO) to create neighborhood exposures of walkability and food environment. A thigh worn accelerometer collected data to measure sedentary time, total daily PA, and MVPA. Dietary quality was measured with a food frequency questionnaire. Multivariate linear regression analyses were adjusted for age, sex, socioeconomic position, neighborhood walkability, and food environment. ResultsThose living in the highest quartile area of perceived social cohesion had statistically significant lower levels of SB (Q4 B: -13.04; 95% CI = −20.23, −5.85), higher total PA (Q4 B: 4.39; 95% CI = 1.69, 7.10), and higher MVPA (Q4 B: 2.57; 95% CI = 0.83, 4.31) and better diet quality (Q4 B: 1.12; 95% CI = 0.24, 2.01) compared to the lowest quartile independent of walkability and food environment. Similar results were found using the Livability meter. ConclusionWe discovered neighborhood social cohesion as an important obesogenic determinant that should be considered in policymaking to encourage higher levels of PA and higher diet quality.

The Impact of GCP Chip Distribution on Kompsat-3A RPC Bias Compensation

The vast potential of high-resolution satellite images, including Kompsat-3A, has been demonstrated across diverse applications, such as mapping and disaster monitoring. However, these images can only be utilized as reliable GIS (geographic information system) data when they possess precise geographical information. To achieve this, sensor model information, represented by RPCs (rational polynomial coefficients), requires bias compensation through GCPs (ground control points). Though having a substantial number of well-distributed GCPs across satellite images is ideal, the acquisition process is often restricted due to cost and inaccessibility. The uniform distribution of GCP chips is not guaranteed, necessitating an investigation into the impact of GCP distribution on the bias compensation process, which is the focus of this study. Experiments were meticulously conducted using Kompsat-3A data using dense GCP information. The dense GCP information was automatically generated from aerial orthoimages through a three-step process. Firstly, the GCP chips were extracted from the aerial images, focusing on feature points. Secondly, these chips were projected onto the target Kompsat-3A data to align them accurately. Lastly, precise satellite image coordinates of the chips were obtained through image matching between the chips and the target Kompsat-3A image. The dense GCPs enabled detailed bias analysis that exhibited skewness in most Kompsat-3A data. This necessitates the implementation of an affine model for proper bias compensation over the entire image space. Next, the study delved into the influence of GCP distribution on RPC bias compensation. To this end, each target satellite image space was divided into nine zones, with the dense GCPs assigned accordingly. The accuracy of bias compensation was analyzed across nine experimental cases, ranging from GCPs occupying only one zone to GCPs covering all nine zones. It was observed that GCPs covering at least four or five zones should be utilized for reliable RPC bias compensation of Kompsat-3A, especially when aiming for a high level of accuracy with an RMSE of one pixel. Finally, it was concluded that GCPs covering three zones yielded satisfactory results as a minimum GCP requirement, but this was contingent upon their distribution not following a straight zone pattern.

Integration of Geographic Information Systems and Spatial Data Analysis in Location Decision Making for Manufacturing Industries

Integration of Geographic Information Systems and Spatial Data Analysis in Location Decision Making for Manufacturing Industries

Predictive Modeling of Fire Incidence Using Deep Neural Networks

To achieve successful prevention of fire incidents originating from human activities, it is imperative to possess a thorough understanding. This paper introduces a machine learning approach, specifically utilizing deep neural networks (DNN), to develop predictive models for fire occurrence in Keelung City, Taiwan. It investigates ten factors across demographic, architectural, and economic domains through spatial analysis and thematic maps generated from geographic information system data. These factors are then integrated as inputs for the DNN model. Through 50 iterations, performance indices including the coefficient of determination (R2), root mean square error (RMSE), variance accounted for (VAF), prediction interval (PI), mean absolute error (MAE), weighted index (WI), weighted mean absolute percentage error (WMAPE), Nash–Sutcliffe efficiency (NS), and the ratio of performance to deviation (RPD) are computed, with average values of 0.89, 7.30 × 10−2, 89.21, 1.63, 4.90 × 10−2, 0.97, 2.92 × 10−1, 0.88, and 4.84, respectively. The model’s predictions, compared with historical data, demonstrate its efficacy. Additionally, this study explores the impact of various urban renewal strategies using the DNN model, highlighting the significant influence of economic factors on fire incidence. This underscores the importance of economic factors in mitigating fire incidents and emphasizes their consideration in urban renewal planning.

Interplay and Implications of Urban Renewal under Dual Laws - An Agent-Based Simulation

Abstract This article focuses on the impact of the simultaneous implementation of dual laws on urban renewal implementation. Especially when the two laws have different case success rates. We refer to the agent-based model used by Acevedo et al. (2003) for the Los Angeles City Development Study. We used the Geographic Information System (GIS) data to replace the virtual grid. We selected the data of Taipei City for simulation. Compare with the "Urban Renewal Act" and the "Statute for Expediting Reconstruction of Urban Unsafe and Old Buildings" that are currently being implemented simultaneously. Simulations and studies were conducted based on the case success rate and the gap between the two laws. The study results show that only by widening the gap between the two laws can the "Urban Renewal Act" be used at a higher rate. This means clearer enforcement scope and conditions should be set in both regulations to avoid direct conflict. JEL classification numbers: C15, C63, L38, P25, R52. Keywords: Urban Renewal, Agent-based modeling, NetLogo, GIS.

Integrating GIS and BIM with MCDM for infrastructure planning: a comprehensive framework

PurposePresent study aimed to integrate Geographic Information Systems (GIS) and Building Information Modeling (BIM) in conjunction with multicriteria decision-making (MCDM) to enhance infrastructure investment planning.Design/methodology/approachThis analysis combines GIS databases with BIM simulations for a novel highway project. Around 150 potential alternatives were simulated, narrowed to 25 more effective routes and 3 options underwent in-depth analysis using PROMETHEE method for decision-making, based on environmental, cost and safety criteria, allowing for comprehensive cross-perspective comparisons.FindingsA comprehensive framework proposed was validated through a case study. Demonstrating its adaptability with customizable parameters. It aids decision-making, cost estimation, environmental impact analysis and outcome prediction. Considering these critical factors, this study holds the potential to advance new techniques for assessment and planning railways, power lines, gas and water.Research limitations/implicationsThe study acknowledges limitations in GIS data quality, particularly in underdeveloped areas or regions with limited technology access. It also overlooks other pertinent variables, like social, economic, political and cultural issues. Thus, conclusions from these simulations may not entirely represent reality or diverse potential scenarios.Practical implicationsThe proposed method automates decision-making, reducing subjectivity, aids in selecting effective alternatives and considers environmental criteria to mitigate negative impacts. Additionally, it minimizes costs and risks while demonstrating adaptability for assessing diverse infrastructures.Originality/valueBy integrating GIS and BIM data to support a MCDM workflow, this study proposes to fill the existing research gap in decision-making prioritization and mitigate subjective biases.

Criminological Insights: A Comprehensive Spatial Analysis of Crime Hot Spots of Property Offenses in Malaysia's Urban Centers

This study examines property crime trends in Selangor, Kuala Lumpur, and Putrajaya from 2015 to 2020, utilizing Geographic Information Systems (GIS) and comprehensive crime data from the Royal Malaysia Police. It focuses on the relationship between property crime rates and the delineation of police station jurisdictions across Selangor State, Kuala Lumpur Federal Territory (KLFT), and Putrajaya Federal Territory (PFT). The objective is to understand the dynamics of property crime, its evolution, and the influence of the National Transformation Program on crime prevention strategies. By assessing the spatial distribution of property crimes, this research aims to identify underlying trends and the efficacy of hot spot policing. The analysis is underpinned by an extensive review of secondary data from the Intelligence/Operations/Records Division of the Royal Malaysia Police Headquarters, enabling a deep dive into the spatial characteristics of property crime within these regions. The findings contribute to the broader understanding of crime prevention in Malaysia, underscoring the importance of GIS technology and the need for strategic interventions tailored to specific crime patterns. This study provides critical insights for policymakers and law enforcement to formulate evidence-based strategies, enhance public safety, and inform future crime prevention efforts.

Modeling on comprehensive evaluation of groundwater quality status using Geographic Information System (GIS) and Water Quality Index (WQI): a case study of Bahir Dar City, Amhara, Ethiopia

Abstract Groundwater is the most important natural resource, and many people throughout the world rely on it for drinking, particularly in rural areas. The present study was carried out to assess the status of groundwater quality and to check its suitability for domestic purposes in urban centres of Bahir Dar City, Ethiopia. Twelve shallow wells were selected for sampling. The sampled water was taken during the dry and summer seasons of the year 2019/2020. pH, turbidity, EC, TDS, chloride (Cl−), nitrate (NO3-), phosphate (PO4-3), total hardness, and Escherichia coli were measured for the suitability analysis. Comparison of measured results with that of WHO and Ethiopian drinking water quality standards wasdone. Moreover, Geographic Information System (GIS) and Water Quality Index (WQI) data analysis techniques were applied in order to investigate the groundwater quality. The spatial distribution map showed that the city's core area had the poorest groundwater quality status. The WQI result obtained from the analysis showed that 41.67, 33.33, and 25% of the sampled groundwater has low, extremely poor, and unsafe quality for drinking purposes, respectively. The present study revealed that anthropogenic activities have a great impact on the quality of groundwater in the area, necessitating immediate mitigating actions.

Exploring Virtual Environments to Assess the Quality of Public Spaces

Human impression plays a crucial role in effectively designing infrastructures that support active mobility such as walking and cycling. By involving users early in the design process, valuable insights can be gathered before physical environments are constructed. This proactive approach enhances the attractiveness and safety of designed spaces for users. This study conducts an experiment comparing real street observations with immersive virtual reality (VR) visits to evaluate user perceptions and assess the quality of public spaces. For this experiment, a high-resolution 3D city model of a large-scale neighborhood was created, utilizing Building Information Modeling (BIM) and Geographic Information System (GIS) data. The model incorporated dynamic elements representing various urban environments: a public area with a tramway station, a commercial street with a road, and a residential playground with green spaces. Participants were presented with identical views of existing urban scenes, both in reality and through reconstructed 3D scenes using a Head-Mounted Display (HMD). They were asked questions related to the quality of the streetscape, its walkability, and cyclability. From the questionnaire, algorithms for assessing public spaces were computed, namely Sustainable Mobility Indicators (SUMI) and Pedestrian Level of Service (PLOS). The study quantifies the relevance of these indicators in a VR setup and correlates them with critical factors influencing the experience of using and spending time on a street. This research contributes to understanding the suitability of these algorithms in a VR environment for predicting the quality of future spaces before occupancy.

A Single Data Extraction Algorithm for Oblique Photographic Data Based on the U-Net

In the automated modeling generated by oblique photography, various terrains cannot be physically distinguished individually within the triangulated irregular network (TIN). To utilize the data representing individual features, such as a single building, a process of building monomer construction is required to identify and extract these distinct parts. This approach aids subsequent analyses by focusing on specific entities, mitigating interference from complex scenes. A deep convolutional neural network is constructed, combining U-Net and ResNeXt architectures. The network takes as input both digital orthophoto map (DOM) and oblique photography data, effectively extracting the polygonal footprints of buildings. Extraction accuracy among different algorithms is compared, with results indicating that the ResNeXt-based network achieves the highest intersection over union (IOU) for building segmentation, reaching 0.8255. The proposed “dynamic virtual monomer” technique binds the extracted vector footprints dynamically to the original oblique photography surface through rendering. This enables the selective representation and querying of individual buildings. Empirical evidence demonstrates the effectiveness of this technique in interactive queries and spatial analysis. The high level of automation and excellent accuracy of this method can further advance the application of oblique photography data in 3D urban modeling and geographic information system (GIS) analysis.

3D VISIBILITY ANALYSES USING BIM & GIS INTEGRATION

Abstract. Visibility is a significant parameter for defining, developing, and evaluating space in the AEC (Architecture, Engineering, and Construction) Industry. Therefore, visibility is considered when designing visually comfortable buildings. Visibility is also a significant parameter in building appraisal. Therefore, in this study, visibility was examined through 3-dimensional (3D) isovists, which are thought to represent the human perception of vision. Visibility was examined regarding spatial openness and mathematically calculated “visual openness indexes” were developed for building valuation. The analysis area was selected in Istanbul, and the urban model was created by using Geographic Information System (GIS) data in Building Information Modelling (BIM) software. Due to, economic reasons affecting different stakeholders in building valuation, it is necessary to create an accurate urban model. For this reason, in the study, the process of obtaining current and accurate GIS data covering the world from open sources is preferred. Then, the process of correctly transferring GIS data to BIM is explained. A sufficient urban model for analysis is created. To calculate the openness indexes, the dormitory in the Golden Horn was modelled in BIM, and different rooms' openness indexes were calculated. Some solutions have been presented to ensure the modelling process with large file sizes and in the calculation of visibility factors. To allow different AEC stakeholders to calculate these indexes, modelling, and calculation is done within BIM. Eventually, it was calculated how much the building users cognitively perceive the openness of the view, and a mathematical comparison of the different rooms' openness indexes was provided.

Climatic zoning of yerba mate and climate change projections: a CMIP6 approach.

Yerba mate (Ilex paraguariensis) is renowned for its nutritional and pharmaceutical attributes. A staple in South American (SA) culture, it serves as the foundation for several traditional beverages. Significantly, the pharmaceutical domain has secured numerous patents associated with this plant's distinctive properties. This research delves into the climatic influence on yerba mate by leveraging the CMIP6 model projections to assess potential shifts brought about by climate change. Given its economic and socio-cultural significance, comprehending how climate change might sway yerba mate's production and distribution is pivotal. The CMIP6 model offers insights into future conditions, pinpointing areas that are either conducive or adverse for yerba mate cultivation. Our findings will be instrumental in crafting adaptive and mitigative strategies, thereby directing sustainable production planning for yerba mate. The core objective of this study was to highlight zones optimal for Ilex paraguariensis cultivation across its major producers: Brazil, Argentina, Paraguay, and Uruguay, under CMIP6's climate change forecasts. Our investigation encompassed major producing zones spanning the North, Northeast, Midwest, Southeast, and South of Brazil, along with the aforementioned countries. A conducive environment for this crop's growth features air temperatures between 21 to 25°C and a minimum precipitation of 1200mm per cycle. We sourced the current climate data from the WorldClim version 2 platform. Meanwhile, projections for future climatic parameters were derived from WorldClim 2.1, utilizing the IPSL-CM6A-LR model with a refined 30-s spatial resolution. We took into account four distinct socio-economic pathways over varying timelines: 2021-2040, 2041-2060, 2061-2081, and 2081-2100. Geographic information system data aided in the spatial interpolation across Brazil, applying the Kriging technique. The outcomes revealed a majority of the examined areas as non-conducive for yerba mate cultivation, with a scanty 12.25% (1.5 million km2) deemed favorable. Predominantly, these propitious regions lie in southern Brazil and Uruguay, the present-day primary producers of yerba mate. Alarming was the discovery that forthcoming climatic scenarios predominantly forecast detrimental shifts, characterized by escalating average air temperatures and diminishing rainfall. These trends portend a decline in suitable cultivation regions for yerba mate.

Interlinking BIM and GIS data for a semantic pedestrian network and applications in high-density cities

In high-density cities, pedestrians frequently traverse many publicly accessible indoor spaces, such as metro stations and footbridges, which seamlessly connect with outdoor sidewalks, forming indoor-outdoor combined pedestrian networks. However, the information island phenomenon hinders these connections in the digital world because outdoor sidewalks and indoor spaces are modelled using Geographical Information Systems (GIS) and Building Information Modelling (BIM) technologies, respectively, which challenges obtaining integrated information for intelligent pedestrian services. This study presents an approach for interlinking BIM and GIS data for a semantic pedestrian network using semantic web technologies. The proposed approach automatically converts BIM and GIS data into linked data and establishes interlinkages between BIM and GIS data through semantic queries and inferences. The resulting semantic pedestrian network based on integrated linked data graphs forms a knowledge base, including topological and geometrical information and abundant semantic information from BIM and GIS datasets. The application potential of the semantic pedestrian network is demonstrated through information query and semantic route planning. This research contributes to establishing indoor-outdoor combined semantic pedestrian networks ensuring seamless data integration.

Mapping of soil suitability for medicinal plants using machine learning methods

Inadequate conservation of medicinal plants can affect their productivity. Traditional assessments and strategies are often time-consuming and linked with errors. Utilizing herbs has been an integral part of the traditional system of medicine for centuries. However, its sustainability and conservation are critical due to climate change, over-harvesting and habitat loss. The study reveals how machine learning algorithms, geographic information systems (GIS) being a powerful tool for mapping and spatial analysis, and soil information can contribute to a swift decision-making approach for actual forethought and intensify the productivity of vulnerable curative plants of specific regions to promote drug discovery. The data analysis based on machine learning and data mining techniques over the soil, medicinal plants and GIS information can predict quick and effective results on a map to nurture the growth of the herbs. The work incorporates the construction of a novel dataset by using the quantum geographic information system tool and recommends the vulnerable herbs by implementing different supervised algorithms such as extra tree classifier (EXTC), random forest, bagging classifier, extreme gradient boosting and k nearest neighbor. Two unique approaches suggested for the user by using EXTC, firstly, for a given subregion type, its suitable soil classes and secondly, for soil type from the user, its respective subregion labels are revealed, finally, potential medicinal herbs and their conservation status are visualised using the choropleth map for classified soil/subregion. The research concludes on EXTC as it showcases outstanding performance for both soil and subregion classifications compared to other models, with an accuracy rate of 99.01% and 98.76%, respectively. The approach focuses on serving as a comprehensive and swift reference for the general public, bioscience researchers, and conservationists interested in conserving medicinal herbs based on soil availability or specific regions through maps.

A Framework for Data Collection and Management to Support the Comprehensive Development of African Agriculture

Agriculture is a key source of food and health for humanity. However, the hazardous way this activity is still cared for in some developing countries like the Republic of Benin, with poor quality farming data collection mechanisms, can guarantee neither quality and sustainability of production, nor comprehensiveness and efficiency of agricultural interventions. Such shortcomings ineluctably contribute to the limited success of the Comprehensive African Agriculture Development Program (CAADP) promoted by the African Union and its regional chapters, in most African countries. Based on these preliminary remarks, this paper suggests a systematic and automated monitoring, evaluation, learning, and adaptation (MEL) framework that will further be tested, consolidated, validated, and promoted, first in the Republic of Benin, and later in other African countries. This framework is made of local, regional, and national geographic information systems (GIS) which will further serve as databases for planning and interventions in the agricultural sector. The proposed GIS superposes lands’ details (sizes, soils’ characteristics, rainfall data, etc.), stakeholders’ details (demographics, crops, livestock, agricultural practices, etc.), and agricultural value chains’ details. Local, regional, and national agricultural value chains’ development platforms will bring stakeholders together, and serve as sources and co-users of the GIS data. Local, regional, and national MEL and GIS management skilled AEAS agents will facilitate systematic data collection and GIS database management. The data collection and treatment tools should allow the use of all kinds of data formats, including voice and image records, given the oral orientation of most agricultural stakeholders. This original idea emerged from observations during national and regional field experiences, and from the literature that revealed the efficiency of automated real-time and quality data for comprehensive and evidence-based decision-making in development interventions.

Examining rehabilitation access disparities: an integrated analysis of electronic health record data and population characteristics through bivariate choropleth mapping.

Despite efforts to view electronic health records (EHR) data through an equity lens, crucial contextual information regarding patients' social environments remains limited. Integrating EHR data and Geographic Information Systems (GIS) technology can give deeper insights into the relationships between patients' social environments, health outcomes, and geographic factors. This study aims to identify regions with the fastest and slowest access to outpatient physical therapy services using bivariate choropleth maps to provide contextual insights that may contribute to health disparity in access. This was a retrospective cohort study of patients' access timelines for the first visit to outpatient physical therapy services (n = 10,363). The three timelines evaluated were (1) referral-to-scheduled appointment time, (2) scheduled appointment to first visit time, and (3) referral to first visit time. Hot and coldspot analyses (CI 95%) determined the fastest and slowest access times with patient-level characteristics and bivariate choropleth maps that were developed to visualize associations between access patterns and disadvantaged areas using Area Deprivation Index scores. Data were collected between January 1, 2016 and January 1, 2020. EHR data were geocoded via GIS technology to calculate geospatial statistics (Gi∗ statistic from ArcGIS Pro) in an urban area. Statistically significant differences were found for all three access timelines between coldspot (i.e., fast access group) and hotspot (i.e., slow access group) comparisons (p < .05). The hotspot regions had higher deprivation scores; higher proportions of residents who were older, privately insured, female, lived further from clinics; and a higher proportion of Black patients with orthopaedic diagnoses compared to the coldspot regions. Our study identified and described local areas with higher densities of patients that experienced longer access times to outpatient physical therapy services. Integration of EHR and GIS data is a more robust method to identify health disparities in access to care. With this approach, we can better understand the intricate interplay between social, economic, and environmental factors contributing to health disparities in access to care.