Geographic Information System Software Research Articles

Robust Design of 5th Generation District Heating and Cooling (5GDHC) Systems with Seasonal Thermal Energy Storage via GIS Assessment

This research addresses the need for a method to compare waste heat potential and recovery in 5th Generation District Heating and Cooling (5GDHC) systems. Prosumer buildings, known for enhancing flexibility, are analyzed using Geographic Information Systems (GIS) and image vectorization to evaluate 5GDHC systems. The study predicts the amount of waste heat available with an R2 of 0.96, utilizing Chaikin's algorithm to refine thermal images for automatic recognition of the sources of waste heat. Emphasis is placed on the absence of a method for asset design in 5GDHC, with considerations for annual energy calculations, radial network connections, and energy shares among building types. Recommendations for building connecting in Belgium are proposed, favoring 'B'/'C'-labeled buildings. The research explores the impact of building design on heating and suggests energy savings through regulation strategy changes. Dynamic models for heat pumps aim to reduce errors and emissions. The study connects energy indicators and GIS software, contributing to a top-down design approach in 5GDHC systems. The overall goal is to contribute to decarbonization and reduce CO2 emissions in the energy sector.

Spatial Analysis of Forest Fire Potential in Pangi Binangga Nature Reserve, Parigi Moutong District, Central Sulawesi

This study aims to analyze the potential for forest fires in Pangi Binangga Nature Reserve, Parigi Moutong District, Central Sulawesi, by utilizing geographic information system data and mapping areas based on fire potential. The research method involved field surveys and the analysis of primary and secondary data. Forest fire hazards maps were obtained from the analysis of Landsat satellite images that were classified to provide information on land cover, slope, distance from roads, and categorized based on predetermined weighting categories. The overlay process was conducted using Geographic Information System software. The main findings indicate that the area has diverse land cover, with reed grassland savanna having the highest fire hazards covering an area of 1203.78 ha. Very steep slopes cover 339.94 ha, and the distance from roads and settlements to the highest hazards point is 140 meters. These variables also influence the level of hazards. Implications of this study include the placement of forest fire prevention patrols, the use of weather modification technology, and post-fire support to restore ecosystems.

A GIS-based Approach to Spatially Explicit Mean Monthly Areal Rainfall Estimation of Krishna District, Andhra Pradesh, India

Rainfall analysis is pivotal for understanding hydrological systems, crucial for effective water resource management. In this study spatial rainfall analysis is carried out for Krishna District of Andhra Pradesh using ArcGIS software. Rainfall data for a period of 30 years (1992-2021) was collected from the Directorate of Economics and Statistics, Vijayawada, Andhra Pradesh. Utilizing the spatial interpolation techniques in Geographic Information System (GIS) software, mean monthly areal rainfall was found using the three methods viz., Arithmetic mean, Thiessen’s polygon and isohyet method. This study utilized Geographic Information Systems (GIS) to visualize spatial patterns of rainfall across Krishna district. To account for missing data in the rainfall record, the normal ratio method was implemented. Spatial maps were generated to depict mean monthly areal rainfall variations. The analysis yielded mean monthly areal rainfall for Krishna district as 84.72 mm, 85.22 mm and 83.97 mm using the arithmetic mean, Thiessen polygon and Isohyet methods, respectively. Isohyet method is considered to be more accurate and rainfall is the basic data required for all hydrological analysis as an input layer for runoff generation, water resources planning, evapotranspiration studies, crop planning, flood estimation and water budget estimations etc.

Estimate of the Surface Evaporation from the Tharthar Lake (Buhayrat Tharthar) in Iraq, Based on Remote Sensing Data and Techniques of Geographic Information Systems GIS

Tharthar is the largest water reservoir in Iraq. It is Giving the circumstances of drought and water crisis in Iraq, which is considered a semi-arid region, along with significantly feverish temperatures, especially in the summer. It is expected that evaporation rates will increase. Therefore, it was necessary to determine the water loss due to evaporation from Lake Tharthar. Due to the lack of ground measurement stations providing such data, the study relied on remote sensing data, specifically satellite imagery from Landsat 8 and Landsat 9. These images were used to extract temperature values for Lake Tharthar for the hydrological year extending from October 2022 to September 2023. This was done in conjunction with geographic information system (GIS) software and using ArcMap to calculate the annual evaporation rate and volume of the lake. Spatial modeling of evaporation was conducted for each month using the modified Lambert equation, adapted from the Penman-Cridle equation, which is most suitable for the climatic conditions of Iraq. Using this methodology saved time, effort, and cost, and yielded satisfactory results compared to field measurements of nearby water bodies. Temperature and evaporation rates were calculated at the level of each image pixel (30×30 meters). The research concluded many important findings, including that the total annual evaporation from the lake is estimated at 1881.16 millimeters (2.930 billion cubic meters per year). Accordingly, the annual loss per square kilometer from Lake Tharthar is estimated at 188 million cubic meters. This means that when Lake Tharthar reaches its maximum capacity, expanding the area to 2710 square kilometers, the evaporation loss will increase to 5.089 billion cubic meters per year. This necessitates relevant authorities to take necessary measures to reduce the annual evaporation loss from the lake surface. The study recommends adopting this method in cases where data is not available for locations lacking weather stations or when there is a data shortage.

Landslide Detection Using Analysed UAV Imagery

Landslide is one of the disasters that often occur in Malaysia. It causes thousands of injuries and death records. Plus, a lot of money has been spent on search and rescue purposes and upgrading utilities to prevent landslide occurrence. Malaysia’s climate, categorized as equatorial, is hot and humid throughout the year. Malaysia has been exposed to an average rainfall of 250cm in a year as rainfall has been the main contributor to landslide occurrence. Thus, an early warning system can be convenient to reduce the hazard risk. This research used Unmanned Aerial Vehicles (UAVs) or drones embedded with RGB cameras to capture data. UAVs or drone is a part of technology revolutionary. The UAV has been widely used in different industries including agriculture, mining, land surveyor, and search and rescue. With the UAV capability, in this research, the UAV is used to measure the slope of the earth by capturing orthomosaic data and will be required to process using Geographical Information System (GIS) software to gain access to other data such as imaging data and digital surface model (DSM). With the capability of Pix4D software, the imaging data such as thermal and spectral imaging can be generated. The data will be compared with rainfall data from Jabatan Meteorologi Malaysia to compare the effect of rainfall on slope conditions. The data for the result will be a great contribution to the early warning system as it contributes towards the accuracy of landslide prediction based on the volume of rainfall.

Detection of Urban Growth in Taiz City, Yemen, Between 1981 and 2022 by Using Google Earth™ and Geographical Information System Data

Rapid urbanization brought serious socio-economic and environmental problems in many cities around the world, particularly in developing countries like Yemen. Urban "sprawl", a synonym of rapid and unplanned urban growth, is considered an obstacle for sustainable development, which causes serious long- and short-term problems, such as extreme infrastructure expenses, economic inefficiency, environmental destruction, and social instability. This is the case of Taiz City, the third largest city in Yemen, which is facing a huge challenge to manage the spatial extension of its built-up area at the expense of the reduction of natural resources and environment. This study aims to investigate urban growth process in Taiz City, focusing on urban sprawl and agricultural land-use change during 1981–2022. The data used are high resolution geospatial data, which are the topographic map for 1981 and Google Earth (GE) images for 2003, 2012, and 2022. The methodology adopted was manual digitizing using Geographic Information System (GIS) software to extract urban land use features from the images of the different dates. Four land-use maps were produced and used for calculation the urban sprawl and land-use changes. The urban expansion rate and patterns were also identified in three phases: 1981–2003, 2003–2012, and 2012–2022. The results show that the built-up area increased six times from 6.2 km2 in 1981 to 34 km2 in 2022, while the agriculture area decreased by 77% from 18 km2 in 1981 to 4.2 km2 in 2022. The urban sprawl in Taiz covered about 75% of its municipal area and extended to the agricultural and rural areas. The sprawl index (USI) is measured to be 5.4%, indicating a high degree of sprawl in Taiz. There is no open space left for future development, and the water resources are at risk of population and depletion. The study demonstrated that informal and haphazard expansion must be controlled, a development strategy should be prepared, so that sustainable urban growth can be achieved. The results of this study could be used as a decision support tool for urban management activities. The vector layers produced by this study could provide the required input data for the future urban modeling of the city.

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.

A Usable Encryption Solution for File-Based Geospatial Data within a Database File System

Developing a security solution for spatial files within today’s enterprise Geographical Information System (GIS) that is also usable presents a multifaceted challenge. These files exist in “data silos” of different file server types, resulting in limited collaboration and increased vulnerability. While cloud-based data storage offers many benefits, the associated security concerns have limited its uptake in GIS, making it crucial to explore comparable alternative security solutions that can be deployed on-premise and are also usable. This paper introduces a reasonably usable security solution for spatial files within collaborative enterprise GIS. We explore a Database File System (DBFS) as a potential repository to consolidate and manage spatial files based on its enterprise document management capabilities and security features inherited from the underlying legacy DBMS. These files are protected using the Advanced Encryption Standard (AES) algorithm with practical encryption times of 8 MB per second. The final part focuses on an automated encryption solution with schemes for single- and multi-user files that is compatible with various GIS programs and protocol services. Usability testing is carried out to assess the solution’s usability and focuses on effectiveness, efficiency, and user satisfaction, with the results demonstrating its usability based on the minimal changes it makes to how users work in a collaborative enterprise GIS environment. The solution furnishes a viable means for consolidating and protecting spatial files with various formats at the storage layer within enterprise GIS.

Comparative evaluation of the web-based contiguous cartogram generation tool go-cart.io.

Area cartograms are map-based data visualizations in which the area of each map region is proportional to the data value it represents. Long utilized in print media, area cartograms have also become increasingly popular online, often accompanying news articles and blog posts. Despite their popularity, there is a dearth of cartogram generation tools accessible to non-technical users unfamiliar with Geographic Information Systems software. Few tools support the generation of contiguous cartograms (i.e., area cartograms that faithfully represent the spatial adjacency of neighboring regions). We thus reviewed existing contiguous cartogram software and compared two web-based cartogram tools: fBlog and go-cart.io. We experimentally evaluated their usability through a user study comprising cartogram generation and analysis tasks. The System Usability Scale was adopted to quantify how participants perceived the usability of both tools. We also collected written feedback from participants to determine the main challenges faced while using the software. Participants generally rated go-cart.io as being more usable than fBlog. Compared to fBlog, go-cart.io offers a greater variety of built-in maps and allows importing data values by file upload. Still, our results suggest that even go-cart.io suffers from poor usability because the graphical user interface is complex and data can only be imported as a comma-separated-values file. We also propose changes to go-cart.io and make general recommendations for web-based cartogram tools to address these concerns.

Population analysis using GIS software tools A case study of Chengdu

Population analysis using GIS software tools A case study of Chengdu

Geographical distribution of nitrate pollution and its risk assessment using GIS and Monte Carlo simulation in drinking water in urban areas of Fars province-Iran during 2017-2021.

Pollution of water resources with nitrate is currently one of the major challenges at the global level. In order to make macro-policy decisions in water safety plans, it is necessary to carry out nitrate risk assessment in underground water, which has not been done in Fars province for all urban areas. In the current study, 9494 drinking water samples were collected in four seasons in 32 urban areas of Fars province in Iran, between 2017 and 2021 to investigate the non-carcinogenic health risk assessment. Geographical distribution maps of hazard quotient were drawn using geographical information system software. The results showed that the maximum amount of nitrate in water samples in 4% of the samples in 2021, 2.5% of the samples in 2020 and 3% of the samples in 2019 were more than the standard declared by World Health Organization guidelines (50mg/L). In these cases, the maximum amount of nitrate was reported between 82 and 123mg/L. The HQ values for infants did not exceed 1 in any year, but for children (44% ± 10.8), teenagers (10.8% ± 8.4), and adults (3.2% ± 1.7) exceeded 1 in cities, years, and seasons, indicating that three age groups in the studied area are at noticeably significant non-carcinogenic risk. The results of the Monte Carlo simulation showed that the average value of non-carcinogenic risk was less than 1 for all age groups. Moreover, the maximum HQ values (95%) were higher than 1 for both children and teenager, indicating a significant non-carcinogenic risk for the two age groups.

Disaster management with cloud-based geographic information systems: site selection of landfill areas after Kahramanmaraş, Türkiye earthquake sequence

On February 6, 2023, the earthquakes centered on Kahramanmaraş-Pazarcık and Kahramanmaraş-Elbistan affected several provinces in the region and generated a vast amount of demolition waste. This study aims to develop a site selection model for the most suitable landfill areas after the earthquake using open-source Geographic Information Systems (GIS) software and the Best-Worst Method (BWM). First, the study identifies decision criteria with a Delphi expert panel. Next, it determines the weights using the BWM based on pairwise comparison. Spatial data of the determinants were obtained from open data portals, and proximity, overlay, and slope analyses were carried out for the disaster region. Combining the output data layer of each criterion, a suitability map of the landfills was produced. As a result, 36,2% (29,826 kms square) of the study area was classified as suitable and very suitable. In this study, a cloud GIS-based decision support platform was developed to act quickly and initiate recovery processes within the scope of disaster management of the destructive earthquake. The suitability map and potential sites were shared with the responsible teams in the disaster area so that the potential sites could be displayed on the map and the suitable areas could be navigated on appropriate routes. The findings of the study revealed that the use of open data and cloud GIS framework in the disaster management process enables quick, reliable, and cost-effective actions in the immediate aftermath of disasters.

Proximity to Hospital-Based Obstetric Care in a Maternity Desert in the Deep South.

Objectives. To examine the accessibility of hospital facilities with maternity care services in 1 rural county in Alabama in preparation for the initiation of prenatal care services at a federally qualified health center. Methods. We analyzed driving distance (in miles) from maternal city of residence in Conecuh County, Alabama to hospital of delivery, using 2019-2021 vital statistics data and geographic information system (GIS) software. Results. A total of 370 births to mothers who have home addresses in Conecuh County were reported, and 368 of those were in hospital facilities. The majority of deliveries were less than 30 miles (median = 23 miles) from the maternal city of residence. Some women traveled more than 70 miles for obstetrical care. Conclusions. Pregnant patients in Conecuh County experience significant geographic barriers related to perinatal care access. Using GIS for this analysis is a promising approach to better understand the unique challenges of pregnant individuals in this rural population. Public health policy efforts need to be geographically tailored to address these disparities. (Am J Public Health. 2024;114(S4):S330-S333. https://doi.org/10.2105/AJPH.2024.307692).

Spatial Analysis Leveraging Geographic Information System and Kernel Density Estimation for Crime Prediction

This study explored the potential of spatial analysis techniques to predict crime hotspots and coldspots within the province of Antique, Philippines. The unique blend of geographical features (coastal and mountainous regions) and socio-economic characteristics in Antique presented a compelling opportunity for the research. Crime data from 2018 to 2023, encompassing direct assaults, homicides, murders, and parricides, was acquired from the Police Regional Office 6. Using Quantum Geographic Information System (QGIS) software and Kernel Density Estimation (KDE) algorithms, the researchers analyzed these crime records. The findings highlighted a positive trend – a decrease in direct assaults over the study period. However, homicide data presented a more concerning picture, with specific locations consistently reporting higher rates. While murder cases showed a decline, parricide incidents remained relatively low, with occasional fluctuations. In conclusion, this data-driven approach, utilizing advanced crime analysis tools, offers valuable insights for policymakers in Antique. By investing in such technology and adopting data-driven strategies, policymakers can work towards creating a safer and more secure future for their communities.

Delineation of Groundwater Potential Zones Using Remote Sensing and Gis Techniques of Phewa Watershed (Nepal)

Groundwater is the primary source of water in the study region, both for domestic and agricultural needs. The main objective of this research is to identify the groundwater potential zones via remote sensing (RS) and Geographic information system (GIS), which is essential in evaluating, preserving, and monitoring various groundwater-related development programs. Man-made pressures include over-exploitation of groundwater and climate changes have led to a strain on groundwater resources. As the use of consumable water increases for human consumption, agriculture, industrial growth, municipal, and pharmaceutical needs to evaluate the groundwater potential and aquifer productivity also increase. Groundwater inspections have been historically done by field survey method, which is inefficient or not practical in terms of time and resources. Arc GIS software is utilized to manipulate datasets. The LULC map of the study area will be developed using Landsat 8 satellite data. A soil map and rainfall map will be created using the data sources from USGS, a Parent soil map will creates using the data from NARC and slope map, lineament density and drainage density from ASTER from earth data. Seven thematic maps will be applied, each with appropriate weight and rank assigned based on its characteristics and connection with groundwater. All the thematic layers combine into a GIS domain, and weight values are put to the attribute table for each polygon. The groundwater potential zone map will be classified into different zones, i.e., very poor, poor, moderate, good, and very good. These results will help hydrogeologists, decision-makers, planners, and local authorities formulate better groundwater resource planning in the Phewa watershed area.

SIMULATION OF FLOOD-PRONE AREAS USING MACHINE LEARNING AND GIS TECHNIQUES IN SAMANGAN PROVINCE, AFGHANISTAN

Flood events are the most sophisticated and damaging natural hazard compared to other natural catastrophes. Every year, this hazard causes human-financial losses and damage to croplands in different locations worldwide. This research employs a combination of artificial neural networks and geographic information systems (GIS) to simulate flood-vulnerable locations in the Samangan Province of Afghanistan. First, flood-influencing factors, such as soil, slope layer, elevation, flow direction, and land use/cover, were evaluated as influential factors in simulating flood-prone areas. These factors were imported into GIS software. The Fishnet command was used to partition the information layers. Furthermore, each layer was converted into points, and this data was fed into the perceptron neural network along with the educational data obtained from Google Earth. In the perceptron neural network, the input layers have five neurons and 16 nodes, and the outputs showed that elevation had the lowest possible weight (R2 = 0.713) and flow direction had the highest weight (R2 = 0.913). This study demonstrated that combining GIS and artificial neural networks results in acceptable performance for simulating and modeling flood susceptible areas in different geographical locations and significantly helps prevent or reduce flood hazards.

Remote Sensing and GIS in Air Pollution Mitigation: A Bibliometric Review of Chhattisgarh, India

Remote sensing and GIS serve as indispensable tools in the ongoing battle against air pollution and in the quest to improve air quality. These technologies, including satellite imagery and aerial mapping, provide a wealth of data pertaining to atmospheric conditions and the presence of pollutants. Through the integration of this data within Geographic Information Systems (GIS) software, researchers and policymakers gain valuable insights into the distribution and concentration of pollutants such as sulphur dioxide, nitrogen oxides, and particulate matter, all of which have significant implications for human health and the environment. The utilization of remote sensing allows for the monitoring of pollutant movement over large geographical areas, enabling the identification of pollution sources and the assessment of their impact on surrounding regions. GIS, on the other hand, facilitates the visualization of this data through the creation of maps and spatial analyses, offering a comprehensive understanding of air pollution patterns and their correlations with demographic characteristics and land use practices. By leveraging these technologies, decision-makers can pinpoint areas with heightened pollution levels, such as urban centers or regions with extensive industrial activity. Armed with this information, targeted mitigation strategies can be devised and implemented to alleviate pollution burdens and safeguard public health. Furthermore, the integrated approach provided by remote sensing and GIS enables the monitoring of the effectiveness of these strategies over time, allowing for adjustments and refinements as needed. In the context of Chhattisgarh, India, where air pollution poses a significant challenge, the application of remote sensing and GIS holds particular promise. By highlighting the advancements and innovations in these fields within the state, we aim to underscore the potential for informed decision-making and proactive measures to combat air pollution and enhance air quality for the benefit of all residents. In this review paper we aim to highlight the advancements in this field that has occurred in controlling air pollution in the state of Chhattisgarh, India.

Poverty Alleviation through Self-Help Groups in Bihar: A case study of Gaya District

This study explores the role of Self-Help Groups (SHGs) in poverty alleviation within Gaya District, Bihar, utilizing data sourced from the Deendayal Antyodaya Yojana-National Rural Livelihoods Mission (DAY-NRLM), Ministry of Rural Development, Government of India. Employing a mixed-methods approach, quantitative analysis of SHG membership trends is complemented by qualitative insights gathered through interviews and focus group discussions with SHG members and stakeholders. Quantitative analysis involves examining data on new and total SHG members across 24 blocks within Gaya District over the past five years. Comparative analysis identifies variations in membership rates among blocks, while geospatial analysis, facilitated by Geographic Information System (GIS) software, visualizes SHG membership distribution. Qualitative insights provide contextual understanding of socio-economic factors influencing SHG participation. Integrated analysis synthesizes findings, elucidating SHG dynamics and their impact on poverty alleviation efforts. Results reveal a notable increase in SHG membership, with approximately 7.88% of the total population joining SHGs in the last five years. Challenges such as inclusivity and access to resources are identified. This study contributes valuable insights to the discourse on SHG effectiveness in poverty alleviation, informing policy formulation and program implementation for sustainable development and poverty reduction in Bihar.

Technoeconomic Analysis of a Wind Power Generation System and Wind Resource Mapping Using GIS Tools: The Case of Twelve Locations in the Commune of Evodoula, Cameroon

This paper performs a technoeconomic analysis of the wind power potential and evaluates the cost of wind power generation in Evodoula, comparing two methods, the conventional method and the uncertainty method based on a comparative spatiotemporal approach using the geographic information system (GIS) software tool. This study is based on satellite wind data measured at 10 m above ground level (AGL) over a 40-year period (1980-2019), by the meteorological service NASA (National Aeronautics and Space Administration)/Goddard Space Flight Center (GSFC). The main objectives are to obtain an appropriate design for a proven optimal location and to assess the viability of wind power at Evodoula. For this type of study, there is little literature available. The optimization of an onshore wind farm and deployment in the wind energy development interest area (ZIDEE) is carried out to obtain a minimum and parsimonious discounted cost production unitary (CPU) of the electricity produced. The results showed that in Okok, a location with a large energy deficit, an onshore wind farm with an electricity generation capacity of 12.5 MW with 5 NORDEX N100-2.5MW wind turbines would have a total energy production (TEP) of about 64.0254825 TWh and a selling price of electricity that would be 0.0034 CAD$/kWh, which is very low compared to the utility price (about 98% cheaper). The total cost of the wind farm would be about $11 million, with a net present cost of about $218 million. The annual profit generated by the wind farm would be over $6 billion. The return on investment (ROI) of the project is estimated at 2880.882%. The constructed onshore wind farm would avoid CO2 emission at over 11 MtCO2,eq/year as the energy generated is from the atmosphere. The wind farm would realize an average annual cash flow estimated at nearly $30 million after 20 years of operation. These savings would allow the installation of CO2 capture systems in conventional power plants. In addition, the analysis of uncertainties and risks was identified and quantified to estimate the confidence levels of the project development results. The risks have been assessed, and we recommend that the total uncertainty of the project is around 15%. The energy values in P75 and P90 are 10.08% and 19.22%, respectively, lower than the energy value in P50 of 11.60 GWh/An. Finally, the main policy recommendations for an inclusive design process are highlighted. The contribution of this study is to assist policy makers in making appropriate decisions in the development and implementation of energy and environmental policy in Cameroon and in many continental areas.

Location-Based Management of Paleontological Collections using Open Source GIS Software

Management of Natural History collections, particularly paleontological collections, is usually structured around specimens. However, it might not be feasible when the collections are incorporated from sources where the whole cycle of specimen collection is unknown, making it hard to determine what fossils belong to a single specimen and if these specimens belong to a single fossil-assemblage, taxon or individual. The Sociedade de História Natural from Torres Vedras (Portugal) manages such collections using a management strategy approach based on the spatial location from where the fossils were found. To achieve this, we have implemented a methodology for field collection and developed a spatial database in PostgreSQL/PostGIS to manage the entire stages of paleontological collections management. On top of the database, a second layer of web services, Web GIS (Geographical Information System) applications, and desktop GIS, all based on QGIS, are also under development to facilitate access to data stored in the database to internal and external researchers and partners, as well public institutions. This effort is made in close collaboration with in-house actors and stakeholders and has yielded promising results. The development of this system provides four advantages: (i) it played an essential role in supporting the transfer of the collection to new storage facilities; (ii) it provides overviews of the distribution of the characteristics of the collections; (iii) it enhances institutional partnerships by providing consistent and reliable data and(iv) it supports the establishment and development of research lines.