#gis services for thematic mapping

[dataservices123]

Discover Land Information Management GIS | AABSyS

Explore how GIS solutions from AABSyS can help you improve the efficiency of your land management. Streamline data collection, mapping, and analysis for better land information management. 

[allindustryforcastreports]

Global Geospatial Analytics Market – $33B (2024) to $56B by 2029, 11.1% CAGR

Segmentation Overview The geospatial analytics market is segmented by:

Type: Surface & field analytics; Geovisualization; Network analysis; Artificial neural networks; Others

Technology: Remote sensing; GPS; GIS; Others

Solutions: Geocoding & reverse geocoding; Reporting & visualization; Thematic mapping & spatial analysis; Data integration & ETL; Others

Applications: Surveying; Disaster risk reduction & management; Medicine & public safety; Climate change adaptation; Predictive asset management; Others

End-Users: Agriculture; Defense & intelligence; Utilities & communication; Automotive; Government; Travel & logistics; Others

Regions: North America; Latin America; Europe; Asia-Pacific; Middle East & Africa To buy the report, click on https://www.datamintelligence.com/buy-now-page?report=geospatial-analytics-market

Market Size & Forecast

The global geospatial analytics market is projected to expand at a CAGR of 12.8% between 2024 and 2031.

Other projections estimate market growth from USD 32.97 billion in 2024 to USD 55.75 billion by 2029.

A broader estimate values the market at USD 114.3 billion in 2024, expected to reach over USD 226.5 billion by 2030.

Introduction & Definition

Geospatial analytics is the process of gathering, interpreting, and visualizing location-based data—drawn from satellites, GPS, mobile devices, sensors, and social media—using GIS, AI, and computer vision. This powerful fusion helps governments and businesses gain real-time insights into transportation, urban planning, agriculture, disaster response, defense, utilities, and logistics.

Market Drivers & Restraints

Key Drivers:

Smart City Expansion: The proliferation of IoT sensors and connected devices in urban infrastructure drives demand for spatial analytics to manage traffic, utilities, public safety, and emergency planning.

Technological Integration: Advances in AI, 5G, satellite imaging, and edge computing enable high-resolution, real-time spatial decision-making.

Enterprise Adoption: Widespread demand for location intelligence across sectors—such as agriculture, defense, utilities, transportation, and retail—boosts comprehensive geospatial integration.

Restraints:

Privacy & Security: Handling sensitive spatial data raises concerns over surveillance, data protection, and regulatory compliance.

Data Complexity: Integrating varied data sources—maps, sensors, satellite imagery—remains a challenge due to formatting and standardization issues.

Cost & Skills Gap: High initial investment and talent shortages for GIS and AI expertise hinder full-scale adoption.

Segmentation Analysis

By Type: Surface & field analytics lead due to applications in topography, hydrology, and asset monitoring. Geovisualization supports urban planning and stakeholder communication.

By Technology: GIS dominates software solutions; GPS and remote sensing—particularly LiDAR, radar, and GNSS—are key data capture technologies.

By Solutions: Thematic mapping and ETL tools are in high demand for data-driven decisions across utilities, logistics, and infrastructure.

By Applications: Surveying, disaster mitigation, climate adaptation, asset management, medicine, and public safety are major application fields.

By End-Users: Agriculture (precision farming), defense (geospatial intelligence), utilities, transportation, government services, and logistics are top verticals.To get a free sample report, click on https://www.datamintelligence.com/download-sample/geospatial-analytics-market

Geographical Insights

North America: Holds the largest market share (~34% in 2024), driven by government and defense investments, smart cities, and GIS adoption.

Europe: Adoption spans from transport and delivery logistics to environmental tracking; EU programs boost earth observation and AI integration.

Asia-Pacific: Fastest-growing region due to rapid urbanization and expansion in countries like China, India, and Japan.

Middle East & Africa: High growth supported by smart city initiatives and infrastructure investments.

Recent Trends or News

AI-Embedded Spatial Tools: Major GIS platforms are embedding AI and machine learning for predictive analysis.

Mobile Mapping & 3D Scanning: Use of LiDAR-equipped vehicles and drones is increasing rapidly in infrastructure and mapping applications.

Pandemic & Disaster Applications: The pandemic accelerated use of geospatial analytics for vaccine distribution, health mapping, and crisis response.

Competitive Landscape

Leading companies in the geospatial analytics market include:

Microsoft

Google

General Electric (GE)

SAP

Salesforce

Precisely

Oracle

RMSI

OmniSci

Maxar Technologies

Hexagon AB

TomTom

Trimble

Esri

CARTO

Orbital Insight

These companies lead through AI-powered tools, cloud-native GIS, satellite imagery, mobile solutions, and strategic acquisitions.

Impact Analysis

Economic Impact: Geospatial analytics streamlines operations—optimizing routes, reducing resource wastage, and enhancing project ROI.

Environmental Impact: Unlocks data for spatial monitoring—supporting climate modeling, land-use mapping, environmental compliance, and disaster mitigation.

Social Impact: Shapes public health response systems, emergency services, and urban planning, while challenging privacy norms.

Technological Impact: Drives growth in cloud GIS, AI-engineered mapping, real-time analytics, and sensor networks, enabling scalable spatial insights.

Key Developments

GeoAnalytics Engine by Esri: An AI-integrated GIS platform for advanced spatial querying and real-time analytics.

Hexagon Captura Launch: Optical sensor-based system enhancing spatial measurement precision.

CADLM Acquisition by Hexagon: Adds simulation and reliability modeling for enhanced engineering workflows.

Orbital Insight Growth: Enhances satellite-based analytics capabilities through new partnerships and investment.

Report Features & Coverage

This market report includes:

Global and regional market sizing (2018–2024) with forecasts to 2031

In-depth segmentation by type, technology, solution, application, industry, and region

Competitive landscape with company profiling

Key trends, opportunities, and growth challenges

SWOT analysis, Porter’s Five Forces, and market attractiveness index

Recent innovations and investment updates

About Us

We are a global market intelligence firm committed to delivering in-depth insights across emerging technologies. Our expertise in geospatial analytics helps clients unlock data-driven innovation, streamline operations, and improve strategic planning across industries. We provide accurate forecasting, custom reports, and actionable guidance tailored to enterprise and government needs.

Contact Us

Email: info@datamintelligence.com

Phone: +1 877 441 4866

[chetan-elets24]

Why Attend the World AI Summit 2025 in Bengaluru? 7 Straightforward Reasons to Show Up

Looking at the endless list of AI events in 2025 and wondering which ones are worth your time? Want to explore what’s next in AI, from big ideas to big deals?

We get it. A lot of summits talk about a big game — flashy decks, overused buzzwords, and very little follow-through.

The World AI Summit 2025, taking place in Bengaluru this September, is built differently. The two-day AI event is a global convergence of innovation, intelligence, and impact. It’s a government-industry initiative, in collaboration with the Department of Electronics, IT, Biotechnology, and Science & Technology, Government of Karnataka, and the Karnataka Digital Economy Mission (KDEM), designed to actually move the AI conversation forward — with results you can see and stories that matter.

Here’s a closer look at why leaders from across industries, including tech leaders, enterprises, startups, government bodies, investors, researchers, and AI enthusiasts, are heading to the World AI Summit 2025 and why it’s worth showing up:

It’s Not Just Government-Backed! It’s Government-Built!

The Department of Electronics, IT, Biotechnology, and Science & Technology, Government of Karnataka isn’t just lending its name — it’s actively shaping the agenda. As the host partner, the government is ensuring the summit aligns with Karnataka’s AI-first vision, focussing on digital public infrastructure, AI-driven governance, and policy-backed innovation.

From enabling smart classrooms to reimagining rural governance, the state is actively investing in future-ready solutions. Here are some of the key key AI-for-Governance initiatives from Karnataka’s Budget 2025 highlights:

a. ₹50 Cr Centre for Applied AI for Tech Solutions (CATS): A dedicated centre to develop AI solutions for public service delivery and governance.

b. Kalika Deepa — AI-Powered Learning in 2,000 Schools: Leveraging AI to personalize and improve learning outcomes in government schools across Karnataka.

c. ₹150 Cr K-GIS 2.0: Upgrading the Karnataka Geographic Information System using AI/ML for mapping 29,000 villages, enabling smarter planning and rural development.

At the World AI Summit, policy-makers and innovators will sit at the same table to create real-world impact and synergy.

2. KDEM Brings the Firepower to Scale Big Ideas

As the strategic partner of the World AI Summit, Karnataka Digital Economy Mission (KDEM) is focussed on real outcomes, not just conversations.

Here’s how KDEM is making AI work on the ground:

Startup Scale-Up: Through the Beyond Bengaluru initiative, KDEM is nurturing tech startups in Tier II & III cities with funding access, mentorship, and infrastructure support.

Skilling for the Future: KDEM is enabling industry-aligned digital skills through Future Digital Jobs@Karnataka, preparing the workforce for AI, cloud, and emerging tech roles.

Global B2B Connect: By creating platforms like Karnataka Global Start-Up Grid, it connects local startups with global investors and enterprises.

Ecosystem Building: Partnering with accelerators, tech parks, and research institutions to grow Karnataka’s deep-tech ecosystem beyond metros.

KDEM isn’t just supporting ideas! It’s helping them grow, scale, and succeed globally.

3. The Most Futuristic AI & DeepTech Gathering

We’re bringing together 200+ speakers from global tech giants, policy think tanks, leading universities, and trailblazing startups — all in one place. Expect to connect with over 2,000 delegates diving deep into crucial thematic tracks and 40+ sessions that cut through the noise and get real about what’s working in AI. From enterprise growth to responsible innovation, we will cover all!

4. Here Conversations will Turn Into Deals

If you’re a founder looking to raise funds, an enterprise scouting new solutions, or an investor looking for the next breakout — this is where you’ll find your people.

We’re bringing together 50+ VCs, angel investors, corporate accelerators, and government funding bodies — all under one roof, ready to connect, back big ideas, and get real deals.

5. No Fluff — Just What’s Working in AI

We’ve all heard enough high-level hype. This summit focusses on real AI use cases already in play across industries including banking, logistics, healthcare, education,, and governance, and more. You’ll see what’s been built, what’s working, and where the real gaps and opportunities are.

6. Awards & Expo That Celebrate What Matters

The World AI Awards spotlight innovators who are actually moving the needle. Our AI Innovation Expo is not just a product showcase but a peek into how AI is reshaping work, security, public services, and daily life.

Explore the Dedicated Startup Zone showcasing cutting-edge AI ventures and emerging tech companies, alongside the University AI Lab Pavilion featuring top Indian research and innovation projects.

7. It’s Not Just Talks. It’s Real Connection.

It’s not just about big talks on big stages — it’s about real conversations that matter. From roundtables and startup huddles to investor lounges and off-stage chats, this is where meaningful connections happen and real partnerships begin.

Secure your space at the action-packed World AI Summit 2025 today — worldaisummit.com!

[souhaillaghchimdev]

Geographic Information Systems (GIS) Development

Geographic Information Systems (GIS) have revolutionized the way we interact with spatial data. From city planning to environmental monitoring and logistics, GIS is a powerful tool that combines maps with data for smarter decision-making. In this post, we'll explore what GIS is, the technologies involved, and how to get started with GIS application development.

What is GIS?

GIS stands for Geographic Information Systems, which are tools and systems used to capture, store, analyze, manage, and visualize spatial or geographic data. These systems are essential for analyzing patterns, relationships, and geographic trends across various fields.

Applications of GIS

Urban Planning: Design infrastructure based on population density and land usage data.

Environmental Monitoring: Track climate changes, deforestation, or water quality.

Disaster Management: Map hazard zones and optimize emergency response logistics.

Transportation & Logistics: Optimize routes, manage traffic, and plan delivery systems.

Agriculture: Analyze crop health and soil conditions using satellite imagery.

Key Components of GIS Development

Data: Spatial data (coordinates, maps, images) and attribute data (information about the location).

Maps: Visual representations created from layers of geographic data.

Tools: GIS software and libraries for manipulating and analyzing the data.

Applications: Web or mobile interfaces for users to interact with spatial information.

Popular Tools and Libraries

QGIS: Open-source GIS desktop application for analyzing and visualizing spatial data.

ArcGIS: Enterprise-grade software suite by Esri, used for advanced GIS development.

Leaflet.js: Lightweight JavaScript library for creating interactive maps.

Mapbox: Modern mapping platform with customizable maps and location-based APIs.

GDAL/OGR: Libraries for raster and vector geospatial data manipulation.

PostGIS: A spatial database extender for PostgreSQL.

Basic GIS Development Workflow

Collect Geographic Data: Use satellite data, GPS devices, or public databases like OpenStreetMap.

Preprocess the Data: Clean, convert, and format the data (e.g., shapefiles, GeoJSON, KML).

Store Data: Use spatial databases like PostGIS to manage geodata efficiently.

Develop the Interface: Create web or mobile applications using Leaflet, Mapbox, or OpenLayers.

Analyze Spatial Relationships: Implement queries and analytics to derive insights.

Visualize the Results: Present data in layers, markers, and thematic maps.

Sample Project: Visualizing Population Density with Leaflet.js

<script> var map = L.map('map').setView([40.7128, -74.0060], 11); L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png').addTo(map); // Add GeoJSON layer L.geoJSON(populationData, { style: function (feature) { return { fillColor: getColor(feature.properties.density), weight: 1, opacity: 1, color: 'white', fillOpacity: 0.7 }; } }).addTo(map); </script>

Challenges in GIS Development

Handling large datasets and real-time updates.

Ensuring accuracy and relevance of geographic data.

Managing performance and rendering complex map layers.

Integrating with external services and APIs.

Conclusion

GIS development is a rewarding field that blends programming, data science, and geography. As more industries recognize the value of spatial insights, GIS applications will continue to play a vital role in shaping our world. Whether you're visualizing data on a map or building complex spatial analysis tools, GIS offers endless opportunities for developers to create impactful solutions.

[davidjohnstongisschool]

GIS Services for Accurate Mapping Solutions

In today’s rapidly advancing world, Geographic Information Systems (GIS) have emerged as a cornerstone for accurate mapping and spatial analysis. Whether in urban planning, disaster management, environmental monitoring, or transportation, GIS services play a pivotal role in delivering precise and actionable insights. These services empower industries to harness spatial data and make informed decisions. This blog delves into the various aspects of GIS services and their importance in providing accurate mapping solutions.

What Are GIS Services?

GIS services involve the collection, analysis, management, and visualization of spatial data to support decision-making processes. They provide tools and methodologies for mapping geographic locations and analyzing spatial relationships. From generating detailed maps to conducting spatial analyses, GIS services offer solutions that cater to diverse sectors, such as:

Urban Development: Designing city layouts and managing infrastructure projects.

Environmental Conservation: Monitoring deforestation, tracking wildlife, and managing natural resources.

Disaster Management: Identifying risk zones, planning evacuation routes, and coordinating rescue operations.

Transportation: Optimizing routes, managing traffic, and planning public transport systems.

Importance of Accurate Mapping Solutions

Accurate mapping forms the backbone of effective GIS services. Inaccurate or outdated maps can lead to poor decisions, inefficiencies, and even catastrophic outcomes. For instance, during natural disasters, precise maps are crucial for planning evacuation routes and identifying safe zones. GIS services ensure that maps are not only accurate but also updated in real time, incorporating the latest spatial data for actionable insights.

Components of GIS Services

GIS services encompass several components that work together to deliver accurate mapping solutions:

1. Data Collection

Data is the foundation of GIS. Spatial data can be collected from various sources, including:

Satellite Imagery: Provides high-resolution images of the Earth’s surface.

Aerial Surveys: Captures detailed geographic data through drones or aircraft.

GPS Devices: Offers precise location-based information.

Field Surveys: Gathers ground-level data for enhanced accuracy.

2. Data Processing

Once collected, raw spatial data must be processed to remove errors and ensure consistency. This involves:

Georeferencing to align data with geographic coordinates.

Data cleaning to eliminate redundancies and inaccuracies.

Conversion into compatible formats for integration into GIS platforms.

3. Spatial Analysis

Spatial analysis helps derive meaningful insights from geographic data. Techniques such as overlay analysis, proximity analysis, and network analysis allow GIS professionals to:

Identify patterns and trends.

Assess relationships between geographic features.

Make predictions based on historical data.

4. Visualization

GIS services excel in transforming complex data into visually appealing and easily interpretable maps and charts. This includes:

Thematic maps that highlight specific attributes like population density or land use.

3D visualizations for urban planning and terrain analysis.

Interactive dashboards that allow users to explore data dynamically.

5. Data Management

Efficient data management is crucial for maintaining the accuracy and relevance of GIS solutions. This involves storing, organizing, and updating spatial data using tools like:

Geographic databases such as PostGIS or ArcGIS Online.

Cloud-based storage for scalable and secure data management.

Applications of GIS Services in Accurate Mapping

GIS services find applications across a multitude of industries, offering unparalleled accuracy and insights. Here are some key areas where GIS is making a significant impact:

1. Urban Planning and Development

Urban planners use GIS to design sustainable cities, optimize land use, and manage infrastructure projects. Accurate maps help planners visualize spatial constraints and opportunities, enabling data-driven decisions.

2. Environmental Monitoring

GIS services play a critical role in tracking environmental changes, such as deforestation, climate change, and pollution levels. By analyzing spatial data, organizations can implement effective conservation strategies and monitor their impact.

3. Disaster Management

In disaster management, GIS services provide critical support by identifying risk zones, predicting the impact of natural calamities, and planning evacuation routes. Accurate mapping helps minimize damage and save lives.

4. Transportation and Logistics

GIS services enable efficient route planning, traffic management, and supply chain optimization. For example, logistics companies can use GIS to reduce delivery times and costs by identifying the most efficient routes.

5. Healthcare and Public Health

In the healthcare sector, GIS services assist in mapping disease outbreaks, planning healthcare facility locations, and analyzing access to medical services. This spatial data is invaluable for public health initiatives and resource allocation.

6. Natural Resource Management

GIS services are extensively used in managing natural resources like forests, water, and minerals. Accurate mapping ensures sustainable resource utilization and helps monitor environmental impacts.

Benefits of GIS Services for Accurate Mapping

Improved Decision-Making: GIS services provide a comprehensive understanding of spatial data, enabling better decision-making.

Cost-Effectiveness: By optimizing resources and operations, GIS services reduce costs in sectors like transportation and urban planning.

Real-Time Updates: Many GIS platforms offer real-time data integration, ensuring that maps and analyses remain relevant.

Enhanced Collaboration: GIS services facilitate collaboration by providing a shared platform for multiple stakeholders to access and analyze data.

Scalability: GIS solutions can scale to accommodate growing datasets and expanding project requirements.

Future Trends in GIS Services

The field of GIS is constantly evolving, with new technologies enhancing the accuracy and usability of mapping solutions. Some emerging trends include:

Artificial Intelligence (AI) and Machine Learning (ML): These technologies are being integrated with GIS to automate data processing, enhance spatial analysis, and predict future trends.

3D Mapping and Visualization: Advanced 3D mapping techniques provide a more realistic representation of geographic data, aiding in urban planning and disaster management.

Cloud-Based GIS: Cloud platforms are making GIS services more accessible, scalable, and cost-effective for organizations of all sizes.

Internet of Things (IoT): IoT devices are generating massive amounts of real-time spatial data, which can be integrated into GIS for dynamic analysis and decision-making.

Augmented Reality (AR): AR is being used to overlay GIS data on the real world, offering innovative applications in navigation and field operations.

Challenges in Implementing GIS Services

Despite their numerous benefits, implementing GIS services comes with its own set of challenges:

Data Quality: Ensuring the accuracy and reliability of spatial data can be difficult, especially when integrating data from multiple sources.

Technical Expertise: GIS services require skilled professionals who understand both the technical and analytical aspects of the field.

Cost of Implementation: High initial costs for software, hardware, and data acquisition can be a barrier for some organizations.

Data Privacy: Managing sensitive geographic data requires robust security measures to prevent unauthorized access.

Conclusion

GIS services are revolutionizing the way we understand and interact with geographic data. By providing accurate mapping solutions, they enable organizations to tackle complex challenges, optimize operations, and drive sustainable growth. As technology continues to advance, GIS services will play an even more critical role in shaping our world. Whether you’re in urban planning, environmental conservation, or transportation, leveraging GIS services can be the key to unlocking new opportunities and achieving your goals.

[paulward]

Comprehensive GIS Services: Transforming Data into Actionable Insights

In a world where data drives decision-making, Geographic Information System (GIS) services have emerged as indispensable tools for businesses, governments, and organizations alike. GIS services encompass a vast array of tools, technologies, and techniques that allow users to visualize, analyze, and interpret spatial data. By bridging the gap between raw data and actionable insights, comprehensive GIS services enable users to understand complex relationships and trends within a geographical context, offering solutions across various industries, including urban planning, environmental management, transportation, and public health.

The Core of GIS Services: Integrating Spatial Data and Technology

At its essence, GIS services focus on integrating spatial data with cutting-edge technology to generate insights that are otherwise difficult to obtain. By capturing, storing, managing, analyzing, and displaying geographical data, GIS enables organizations to see beyond numbers and charts. This visual approach to data provides a geographic context that highlights patterns, trends, and relationships within datasets.

The foundation of GIS is built on spatial data, which represents the physical characteristics of locations and the relationships between them. Spatial data can come in various formats, including vector data, raster data, and geodatabases, which together can store information on boundaries, elevation, and even demographic factors. Comprehensive GIS services combine these data types with technological advancements to create interactive, visual maps, enabling users to make well-informed decisions.

Key Components of Comprehensive GIS Services

Comprehensive GIS services provide a range of functionalities, each designed to address specific needs of an organization:

Data Collection and Management: GIS services start with data acquisition, where organizations collect geospatial data using various methods, including satellite imagery, drones, and GPS. Once collected, this data needs to be stored, processed, and managed effectively. Modern GIS services often employ cloud-based platforms, allowing data to be securely stored and accessed from anywhere in the world.

Spatial Analysis: Spatial analysis lies at the core of GIS, enabling users to study relationships between different datasets. With spatial analysis tools, users can conduct activities like proximity analysis, overlay analysis, and network analysis, which can reveal crucial insights, such as identifying areas of high demand or determining optimal routes for transportation.

Geocoding and Mapping: Geocoding is the process of converting addresses into geographic coordinates, which can then be mapped to provide a visual representation of data points. Mapping, in turn, allows organizations to see the geographical distribution of their data, enabling them to visualize clusters, patterns, and outliers that would otherwise remain hidden.

Data Visualization: Through GIS services, data can be transformed into compelling visuals, such as heat maps, 3D models, and thematic maps. These visualizations simplify complex data, making it accessible to non-technical users and aiding in communicating insights to stakeholders. Visualization helps turn data into actionable insights by enabling users to interact with their data in real-time.

Remote Sensing and Image Analysis: Remote sensing uses satellite or aerial imagery to capture data over vast areas, allowing GIS professionals to monitor changes over time. Image analysis tools process these images, making it possible to track developments in environmental conditions, urban expansion, or disaster impacts. This technology provides a reliable means for tracking real-world changes that impact planning and policy-making.

Predictive Analysis and Modeling: By utilizing machine learning algorithms and predictive models, GIS services can forecast future events or trends. For instance, urban planners can use predictive modeling to estimate population growth in specific areas, enabling proactive planning for housing and infrastructure needs. Predictive GIS models also assist in risk assessment, particularly in areas prone to natural disasters.

Applications of GIS Services Across Industries

GIS services have far-reaching applications across numerous industries, driving efficiency and enabling better decision-making. Here are a few key sectors where GIS plays a transformative role:

Urban Planning and Development: GIS has become a cornerstone in urban planning, helping city planners design sustainable cities. With GIS, planners can analyze population density, land use, and transportation networks to create efficient urban layouts. GIS also aids in identifying optimal locations for infrastructure and services, balancing economic growth with environmental protection.

Environmental Management: Environmental organizations rely on GIS services for ecosystem monitoring, wildlife tracking, and environmental impact assessments. By visualizing deforestation rates, water levels, or pollution sources, GIS helps organizations understand ecological changes and manage conservation efforts effectively.

Transportation and Logistics: GIS enables transportation companies to optimize routes, reduce fuel costs, and improve delivery times. Logistics companies use GIS to create accurate road networks and predict traffic patterns, allowing them to respond dynamically to delays or changes in demand. For public transit systems, GIS helps in planning routes, tracking fleet movements, and improving service efficiency.

Public Health: During pandemics or disease outbreaks, GIS services provide vital information for tracking infection rates and predicting spread patterns. By integrating demographic data with spatial analysis, public health officials can identify at-risk areas, allocate resources effectively, and develop targeted health interventions.

Agriculture: Precision agriculture has greatly benefited from GIS, allowing farmers to monitor crop health, manage water resources, and optimize fertilizer use. With GIS, farmers can create field-specific maps, analyze soil conditions, and make data-driven decisions, ultimately improving yield and sustainability.

Disaster Management and Emergency Response: GIS services play a critical role in disaster preparedness, response, and recovery efforts. By mapping hazard-prone areas and analyzing disaster patterns, emergency responders can develop contingency plans, evacuate high-risk areas, and allocate resources where they’re most needed during an emergency.

Advantages of GIS Services in Decision-Making

GIS services offer several significant advantages for organizations seeking data-driven decision-making solutions:

Enhanced Data Accuracy: GIS collects data from multiple reliable sources, ensuring that the resulting insights are accurate and up-to-date. This accuracy is particularly valuable in sectors where precision is essential, such as healthcare and disaster management.

Increased Efficiency: By automating data processing and analysis, GIS services save time and reduce labor costs. For instance, companies can quickly identify profitable locations for new stores or determine efficient routes for deliveries, improving overall operational efficiency.

Improved Resource Allocation: GIS helps organizations make informed decisions about resource allocation, enabling them to distribute their resources effectively. For instance, GIS allows city planners to decide where to place public amenities based on population density and accessibility.

Better Communication with Stakeholders: GIS visualization tools enable organizations to present complex data in a simplified, visually appealing format, making it easier to communicate findings to stakeholders, investors, or the general public. This transparency fosters trust and facilitates collaboration.

Predictive Capabilities for Proactive Planning: With predictive modeling, GIS services enable organizations to foresee future trends and plan accordingly. This forward-looking approach helps businesses and governments mitigate risks, manage resources effectively, and adapt to changing conditions.

Emerging Trends in GIS Services

The field of GIS is continuously evolving, with new trends shaping its future:

Integration with IoT and Real-Time Data: The Internet of Things (IoT) is adding a new dimension to GIS by enabling real-time data collection from sensors and connected devices. This integration allows for real-time monitoring of dynamic conditions, such as traffic flow or environmental changes, empowering organizations to make timely decisions.

AI and Machine Learning in Spatial Analysis: Artificial intelligence (AI) and machine learning are transforming GIS by enabling advanced analysis of complex data sets. Machine learning models can detect patterns and anomalies that are invisible to traditional analysis methods, enhancing GIS’s predictive capabilities.

3D GIS and Augmented Reality (AR): As 3D modeling and augmented reality become more accessible, GIS is moving beyond two-dimensional mapping. 3D GIS enables users to visualize data in a more immersive and intuitive way, which is particularly useful in urban planning and environmental management.

Cloud-Based GIS Solutions: Cloud-based GIS services provide scalable, accessible platforms for data storage and processing, allowing teams to collaborate remotely and access their data from any location. This shift to the cloud is reducing the need for physical infrastructure, making GIS more accessible to organizations of all sizes.

Conclusion

Comprehensive GIS services have proven to be transformative tools for industries seeking data-driven insights to navigate complex challenges. By turning spatial data into actionable insights, GIS helps organizations make informed decisions, optimize resource allocation, and plan proactively for the future. As technology continues to advance, GIS services will likely become even more powerful, playing a pivotal role in shaping the future of data-driven decision-making across various sectors. For organizations looking to harness the power of spatial data, investing in GIS services is not just an option—it’s a necessity.

[gisciencestudent]

Annotated Bibliography of Relevant Sources

Burgan, R, van Wagtendonk, J, Keane, Robert E, Burgan, Robert, & van Wagtendonk, Jan. (2001). Mapping wildland fuels for fire management across multiple scales: Integrating remote sensing, GIS, and biophysical modeling. International Journal of Wildland Fire, 10(3-4), 301–319. https://doi.org/10.1071/WF01028

This study compares the advantages and disadvantages of four different methods of fuel mapping: field reconnaissance, direct remote sensing, indirect remote sensing and biophysical modeling. None of these methods appear to be highly accurate or consistent in and of themselves. Yet, all of these methods represent a means of collecting data for synthesis within a geographic information system for the purpose of creating a map that is useful to land management. The authors propose a strategy which involves classifying biophysical setting, species composition and stand structure to assign fuel models, but acknowledge the enduring need for sensor technology that can penetrate a forest canopy to effectively analyze complex surface fuels.

Church, Richard, Adams, Benjamin, Bassett, Danielle, & Brachman, Micah L. (2019). Wayfinding during a wildfire evacuation. Disaster Prevention and Management., ahead-of-print(ahead-of-print). https://doi.org/10.1108/DPM-07-2019-0216

This will be an interesting article to read when it is published, because it is an example of using GIS to synthesize and analyze empirical data from a wildfire evacuation for the purpose of helping emergency managers to develop more effective wildfire evacuation plans. The authors used network analysis to compare volunteers’ selected routes with the shortest distance routes available, and found that only 31 percent of evacuees took a shortest distance route, and that factors such as the elevation of exits and downhill slope could have impacted wayfinding processes. Although this study is more of a spatiotemporal snapshot, more generalizable results could be produced with additional research.

Coops, Nicholas C., Ferster, Colin J, & Coops, Nicholas C. (2014). Assessing the quality of forest fuel loading data collected using public participation methods and smartphones. International Journal of Wildland Fire, 23(4), 585–590. https://doi.org/10.1071/WF13173

This study looks at the potential for citizens to contribute forest structure and fuels input data for use in the applied geographic information systems used by land managers. Citizen contributions could be especially helpful in data collection over broad areas because accurate characterization of forest fuels is dependent on frequent field measurements, as fuels are spatially variable, can change rapidly due to changing conditions, and are difficult to sense remotely under dense canopy. Eighteen volunteers were recruited at the University of British Colombia, and nine of those had extensive working experience in either wildfire suppression or fuels management. The volunteers used an app on their smartphones to collect and report data. For most components, professional measurements were only slightly closer to reference measurements than volunteered measurements, however, non-professional participants notably overestimated aspect and slope. Overall, when appropriate training is provided and adequate controls for accuracy are incorporated, this study found volunteer data collection to be suitable to help inform forest management decisions.

Danzer, SR, Watts, JM, Stone, S, Yool, SR, Miller, Jay D, Danzer, Shelley R, … Yool, Stephen R. (2003). Cluster analysis of structural stage classes to map wildland fuels in a Madrean ecosystem. Journal of Environmental Management., 68(3), 239–252. https://doi.org/10.1016/S0301-4797(03)00062-8

The authors of this study highlight the importance of quality baseline fuels data since we are not yet capable of assessing understory fuels with remotely sensed data. This assessment therefore combines field data collections with GIS, remote sensing, and hierarchical clustering to map the variability of fuels within and across vegetation types of 156 plots from a mountain range in the southwestern U.S. Vegetation classification was validated using an independent sample of 479 randomly located points and demonstrated substantial accuracy with a Kappa value of .80. However, the overall map, created by combining the land cover/vegetation type classification and fuel classes within vegetation type classifications received a relatively low Kappa of .50. This reduction in accuracy could be attributed to GPS errors, ecological overlap between adjacent vegetation types, and/or confusion of fuel classes in areas where overstory canopies obscured the understory.

Dean, DJ, Blackard, Jock A, & Dean, Denis J. (1999). Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables. Computers and Electronics in Agriculture., 24(3), 131–151. https://doi.org/10.1016/S0168-1699(99)00046-0

The authors compare an artificial neural networks (ANNs) approach to GIS with a conventional model based on discriminant analysis (DA) with regards to effectiveness at predicting forest cover types. In this study, elevation of each 30x30-m raster cell was obtained directly from USGS digital elevation model (DEM) data. Results demonstrated that both ANN and DA models tended to confuse ponderosa pine, Douglas-fir, and cottonwood/willow cover types with each other, potentially due to geographic proximity. However, overall, the ANN model, with a predictive accuracy of 71.1 percent, was shown to be superior to the DA model, with a predictive accuracy of 58.4 percent.

Eva, E. K. (2010). A method for mapping fire hazard and risk across multiple scales and its application in fire management. Ecological Modelling., 221(1), 2–18. https://doi.org/info:doi/

This study presents an effective approach for mapping fire risk across large, complex geographies containing diverse ecosystems on multiple scales. The author uses FIREHARM, which is a C++ program capable of computing changes in fire characteristics over time using climate data, to predict fuel moisture and corresponding fire behavior, danger, and effects. This model does not provide spatially explicit information concerning fire spread. Instead, it assumes that every pixel or polygon experiences a head fire and simulates the resulting fire characteristics based on ensuing weather factors. A landscape is represented by series of polygons. Each polygon defines an area of similar characteristics (vegetation, fuel, site conditions), is assigned attributes related to fire behavior, and is also assigned a tree list (with attributes of species, diameter, height) which combine to estimate tree mortality for a region. In 2004, FIREHARM was validated by the results of a comparison with 54 sample plots from the Cooney Ridge and Mineral Primm wildfires, producing adequate predictions of fuel consumption within approximately 14 days. The model also had a 60 percent chance of accuracy in predicting canopy vs. non-canopy fire and scorch height and fire severity predictions compared well with observed conditions.

Gatzojannis, S, Galatsidas, S, Kalabokidis, Kostas D, Gatzojannis, Stylianos, & Galatsidas, Spyros. (2002). Introducing wildfire into forest management planning: towards a conceptual approach. Forest Ecology and Management, 158(1-3), 41–50. https://doi.org/10.1016/S0378-1127(00)00715-5

This study explains the process of using a geographic information system to synthesis existing information for the purpose of calculating fire danger and fire resistance per unit area (1 km2) and to map distribution within a forest. First, data is collected, next data is grouped into thematic layers, and finally the layers are synthesized for evaluation. The input data required to produce such a map involves taking an inventory of factors with both horizontal and vertical spatial distribution. Homogeneous information layers, such as landscape, are broken down into a series of variables (vegetation zones, land cover structure, aspect, slope, altitude). External factors include climate, landscape, human impact, and other special factors. Internal factor information layers relate to forest stand structure and describe fire resistance at the forest floor, understory, low crown, middle crown, and high crown levels. The mapping of these factors allows for spatial delineation of fire danger zones which decision makers in determining operational objectives and priorities for large geographical areas.

Ilavajhala, Shriram, Wong, Min Minnie, Justice, Christopher O., Davies, DK, Ilavajhala, S, Min Minnie Wong, & Justice, CO. (2009). Fire Information for Resource Management System: Archiving and Distributing MODIS Active Fire Data. IEEE Transactions on Geoscience and Remote Sensing a Publication of the IEEE Geoscience and Remote Sensing Society., 47(1), 72–79. https://doi.org/10.1109/TGRS.2008.2002076

This paper describes the ways in which the combined technologies of remote sensing and GIS are able to deliver Moderate-resolution Imaging Spectroradiometer (MODIS) active fire data to resource managers and even e-mail customized alerts to users. When used as a mobile service, Fire Information for Resource Management System (FIRMS) is an application that can deliver fire information to field staff regarding potential danger. For example, in South Africa, when a fire is detected either using data from MODIS or from a weather satellite, a text message is sent to relevant personnel who can decide if/what action may be required. This strategy makes satellite-derived FIRMS data more accessible to natural resource managers, scientists, and policy makers who use the data for monitoring purposes and for strategic planning.

Kalabokidis, K. (2013). Virtual Fire: A web-based GIS platform for forest fire control. Ecological Informatics., 16, 62–69. https://doi.org/info:doi/

This research project, supported by the University of Athens in Greece and funded by Microsoft Research, describes the high-tech but user-friendly Virtual Fire system, a web-based GIS platform, which allows firefighting forces to share and utilize real-time data (provided by GPS, satellite, camera) for the purpose of locating resources (vehicles, aircrafts, water tanks) and associated shortest routes, monitoring fire ignition probability, and identifying high risk areas. Data from automatic weather stations also aids in fire prevention and early warning. With the ability to conveniently access this information in synthesized form, managers can design more effective and efficient operational plans. Future considerations involve moving to a cloud-based platform, which would allow for expansion to a broader area and the increased incorporation of mobile devices.

Karlsson Martin, Oskar, Galiana Martin, Luis, Montiel Molina, Cristina, Karlsson Martín, Oskar, & Galiana Martín, Luis. (2019). Regional fire scenarios in Spain: Linking landscape dynamics and fire regime for wildfire risk management. Journal of Environmental Management., 233, 427–439. https://doi.org/10.1016/j.jenvman.2018.12.066

The authors of this study apply socioecological systems theory to the wildfire generations model, which describes and explains the appearance and transformation of large wildfires in relation to landscape dynamics within Mediterranean climatic regions. There is a focus on acknowledging the ways in which humans directly and indirectly affect fire regimes. National forest inventory data and existing maps are the data used to create fire scenarios for the Central Mountain Range in Spain via ArcGIS and SPSS23. Land use and land cover features, which relate certain fuel structures to certain fire behaviors, are assigned to 91 discrete geographical units. The resulting visual comparisons can be used to help managers optimize prevention and suppression strategies.

Koukoulas, Sotirios, Kazanis, Dimitrios, & Arianoutsou, Margarita. (2011). Evaluating Post-Fire Forest Resilience Using GIS and Multi-Criteria Analysis: An Example from Cape Sounion National Park, Greece. Environmental Management., 47(3), 384–397. https://doi.org/10.1007/s00267-011-9614-7

The ability to assess an ecosystem’s resilience, or its capacity to endure disturbances without a state change, is becoming more important in the face of an accelerated decrease in biodiversity and with the projected effects of climate change. This study uses geographic information systems (GIS) to assess post-fire resilience by synthesizing bioindicators, such as forest cover, density, and species richness with geo-indicators, such as fire history, slope, and parent material. The significance of each factor was assessed using sensitivity analysis in order to produce a map of areas at risk- “risk hotspots” – of losing resilience, allowing managers to prioritize resources in restoration efforts.

Kulakowski, D, Veblen, TT, Bigler, Christof, Kulakowski, Dominik, & Veblen, Thomas T. (2005). MULTIPLE DISTURBANCE INTERACTIONS AND DROUGHT INFLUENCE FIRE SEVERITY IN ROCKY MOUNTAIN SUBALPINE FORESTS. Ecology., 86(11), 3018–3029. https://doi.org/10.1890/05-0011

GIS technologies are especially helpful for spatially predicting indicators such as fire severity, which can be the result of complex interactions. This study examines the possible combined effects of interactions between the disturbances of fire, insect outbreaks, and storm blowdown upon fire severity. Pairwise overlay analyses were performed in order to assess these associations. The regression models created, unlike bivariate overlay analysis, allowed for the simultaneous predictions, hypothesis tests and assessment of effects. Results showed that local forest cover type was a significant factor affecting fire spread and severity in the Rocky Mountains, with Spruce-fir stands having the highest probability of burning at high severity. Maps created in GIS show weather variability only significantly affecting fire when fuel build up is sufficient. Pre-fire disturbance and topography were also found to influence burn severity and explain variability.

Michener, W. K. (1997). Quantitatively Evaluating Restoration Experiments: Research Design, Statistical Analysis, and Data Management Considerations. Restoration Ecology : the Journal of the Society for Ecological Restoration., 5(4), 324–337. https://doi.org/10.1046/j.1526-100X.1997.00546.x

Ecological restoration projects (i.e. post-wildfire disturbance) can be very difficult to design and analyze quantitatively due to several factors: experimental units are often heterogeneous, multiple non-uniform treatments may be applied iteratively, replication is difficult or impossible, the effects of extrinsic and intrinsic disturbances may be poorly understood, and the goal of focus is typically the variability in system responses rather than mean responses. This author provides thorough explanations of each of these challenges, along with a variety of ways in which they might be addressed, including via the application of GIS technologies. GIS is described as a powerful tool in relation to this discipline because of its ability to quickly synthesis data using multiple layers, rename and reclassify attributes, analyze spatial coincidence and proximity, and provide quantitative and statistical measurements which can be used to identify potential restoration sites and to visualize and interpret results.

Schroeder, P, Kern, JS, Brown, Sandra L, Schroeder, Paul, & Kern, Jeffrey S. (1999). Spatial distribution of biomass in forests of the eastern USA. Forest Ecology and Management, 123(1), 81–90. https://doi.org/10.1016/S0378-1127(99)00017-1

Biomass is defined by the net difference between photosynthetic production and consumption (respiration, mortality, harvest, herbivory). This measurement is an important indicator of the carbon stored in forests, which can be released as atmospheric carbon into the air during a disturbance (i.e. wildfire) or function as atmospheric carbon sinks during periods of regeneration post-disturbance. While accurate measurements of biomass provide valuable information for decision makers and land managers, large scale estimations can be challenging (remote sensing techniques have met with little success). The authors of this widely-cited study decided to use preciously established methods to convert US forest inventory volume data into above and belowground biomass, downloaded from the USFS Forest Inventory and Analysis (FIA) database, which they then mapped in a geographic information system (GIS) by county. These maps provide a vivid visual representation of forest biomass density patterns over space which can be useful in predicting changes to the global carbon cycle and evaluating potential for increased biomass-carbon storage.

Smith, JE, Weinstein, DA, Laurence, JA, Woodbury, Peter B, Smith, James E, Weinstein, David A, & Laurence, John A. (1998). Assessing potential climate change effects on loblolly pine growth: A probabilistic regional modeling approach. Forest Ecology and Management, 107(1-3), 99–116. https://doi.org/10.1016/S0378-1127(97)00323-X

In this study, a geographic information system was used to integrate regional data including forest distribution, growth rate, and stand characteristics, provided by the USDA Forest Service, with current and predicted climate data in order to produce four different models predicting the potential effects of climate change upon the loblolly pine across the southern U.S. Results indicated a high likelihood of a 19 to 95 percent decrease in growth rates, varying substantially per region and primarily influenced by a relative change in carbon assimilation and CO2 concentrations. In this case, GIS seems particularly useful for synthesizing existing information from regional surveys and account for uncertainties to produce ecological risk assessments at large scales in a way that is useful to policy and decision makers (vs. lengthy reports that are difficult to parse through).

Williams, D, Barry, D, Kasischke, Eric S, Williams, David, & Barry, Donald. (2002). Analysis of the patterns of large fires in the boreal forest region of Alaska. International Journal of Wildland Fire, 11(2), 131–144. https://doi.org/10.1071/WF02023

This study represents the first attempt to spatially correlate the distribution of fire activity in Alaska with climate, topographic, and vegetation cover features using GIS to provide a realistic assignment of fire cycle (frequency) for 11 distinct Alaskan ecoregions (where 96% of all fire activity occurs). GIS technologies can make use of Alaska’s state-wide initiative to digitize maps of fire perimeters from fire events from 1950 to 1999. Perimeter maps are created using a combination of ground and aerial surveys, and aerial photography or satellite imagery. Geospatial analysis showed fire frequency to be influenced by the complex interaction of elevation, aspect, lightening strike frequency, precipitation, forest cover, and growing season temperature.

Please send comments and questions to Melissa Hannah at hannahm@oregonstate.edu or click "Comments Are Welcome" at the top of the page.

[bropiner]

Mapublisher html5

#MAPUBLISHER HTML5 PDF#

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Licenses available as both fixed single and floating.

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MAPublisher is available on Mac and Windows and is compatible with the latest Adobe Illustrator CC 2018 release, ensuring the latest graphic design tools are at your disposal. Combine powerful graphic design tools and the geospatial precision of MAPublisher tools to build visually-rich maps. MAPublisher and Adobe Illustrator together form the complete cartographic workflow-from raw data to publishing a high-quality map and everything in between. Easily and quickly import various data formats, create high-quality map products, and export data to different file formats or publish to print and digital formats, including web maps and maps for mobile devices. It's all about increased productivity for cartographers and GIS professionals. Quickly launch MAPublisher tools to manage map layers, view map attributes, create and edit theme stylesheets, create selection filters, Description points and much more. MAPublisher panels and tools seamlessly integrate into the Adobe Illustrator interface and can be resized, moved, and docked. Take it mobile by exporting map packages compatible with the Avenza Maps app. Need a web map? Export your maps using MAP Web Author to create HTML5-compatible web maps without coding knowledge.

#MAPUBLISHER HTML5 PDF#

Need to go digital? Export geospatial PDF that contain coordinate system and attribute information. Get MAPublisher LabelPro™ which pairs advanced rules to a sophisticated labeling engine for a more efficient and intelligent label placement.Ĭreate visually stunning maps that are print-ready. Use the Label Features tool to place labels to map features automatically and fine-tune labeling with the MAP Tagger and Contour Tagger tools. Use MAP Themes to create thematic maps, designate point symbols, and apply graphic styles based on rules that you create.Ĭreate labels based on data attributes. Save time by styling map data based on its attributes. Create new features on-the-fly, maintain georeferencing when joining data, cropping, trimming, buffering, and simplifying. MAPublisher offers plenty of flexibility when editing map features in Adobe Illustrator. Working with geospatial data doesn't have to be rigid. Fortunately, MAPublisher supports thousands of coordinate systems and projections to help you display data accurately. Using the appropriate map projection is crucial for any map design. Import leading GIS industry formats from Esri, Google, MapInfo, WFS/WMS services, and other GIS sources with all attributes and georeferencing intact and fully editable.

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MAPublisher® cartography software seamlessly integrates more than fifty GIS mapping tools into Adobe Illustrator to help you create beautiful maps.Import industry-standard GIS data formats and make crisp, clean maps with all attributes and georeferencing intact using the Adobe Illustrator design environment. Avenza MAPublisher for Adobe Illustrator 10.6 (MacOSX)Īvenza MAPublisher for Adobe Illustrator 10.6 (MacOSX) | 619 MB

[highbartechnocrat]

ENABLING EFFICIENCY IN SMART CITIES VIA RIGHT TECHNOLOGIES

The integration of modern cities with smart technologies is gaining a lot of popularity lately. According to Arup, a London based multinational, the global market for smart urban services will rise up to $400 billion per annum by next year. Cities like Amsterdam, New York, Dubai and Shanghai have already started using such services. So, it is now more essential than ever to understand this concept and the technologies that drive it.

What are Smart Cities?

Although there is no exact definition, the concept of ‘smart cities’ is used as an umbrella term. It refers to a city that uses data and technology to improve the lives of its citizens and businesses. The primary aim is to bring all departments of the city, such as power supply, sanitation, urban transport, waste management, e-governance, citizen participation, urban mobility, and IT under the ambit of connectivity. This makes it easier and more convenient for citizens to go about their daily lives.

A great example of this is the Amsterdam Smart City initiative. Under this, the Dutch government is running several projects via an interconnected system. It uses wireless devices to enhance the city’s real-time decision-making abilities. As a part of the initiative, they have set up smart energy metres in houses to reduce carbon footprint. They have also introduced smart street lights, which can be controlled remotely. Municipalities can also monitor and broadcast real-time traffic information for easy movement of the public. Similar technologies are being used for developing other cities across the world, including some in India.

Let’s take a detailed look at the key technologies that drive the smart city concept

Information and Communication Technology

ITC acts as an interlink between the public and the government, which is essential to the setup of any smart city. It creates a three-way communication channel between smart solutions, citizens, and the government. This is used to analyse the demand pattern of the state and create a requisite pool of online services. These include remote allocation of parking spaces, online registration of licenses, etc.

Internet of Things

The key technology behind the success of any smart city initiative, whether that’s improving pollution levels or traffic conditions, is the IoT. The IoT is basically a network of physically connected devices, like vehicles or home appliances that enable these ‘things’ to connect and exchange data. This, in turn, creates a convergence between the physical and the digital spaces — via data analytics — leading to improved efficiency (both in public and private sectors), livelihoods and better economic benefits.

Sensors

Sensors are hidden but ubiquitous components of an intelligent control system. They work by converting parameters of a physical nature into an electronic signal. This is either interpreted by humans or fed into an autonomous system for further processing. Control systems function by collecting this data and adjusting their operations. Sensors are thus an indispensable part of any smart city setup.

GeoSpatial Technology

Geospatial technology is an umbrella term used for tools involved in geographical mapping and analysis of human societies. The earliest examples of this are GPS-enabled services like Google Maps and Google Earth. In a smart city setup, this plays an important role. Some practical applications of this technology include generation of thematic maps, providing base maps for Geographical Information Systems (GIS), and providing engineering plans for civil works and urban development.

Blockchain

Although Blockchain is a new entry to the smart city concept, it is being considered a game-changer. This technology is capable of providing unparalleled data security because it allows digital information to be stored and distributed but not copied. Simply put, it is creating the backbone of a new kind of information sharing. Some practical benefits include boosting transparency and leading to better connectivity. Smart contracts are another application of this technology. These help with billing, processing transactions and handling of facilities management. Blockchain can also be used in smart grids to facilitate smoother energy sharing.

Artificial Intelligence

Development of a smart city generates a huge amount of data. So without a system that can compile, analyse and process it, all this data is of no use. And that’s why artificial intelligence is so important in a smart city setup. AI allows the machine to machine interaction by processing the data and making sense out of that. Theoretically, it even possesses the capability of making technology self-reliable and self-sustainable.

ABOUT US:

Highbar Technocrat is one of the leading end-to-end IT solution providers for the Infrastructure, EC&O, Real Estate, and Power sectors. They are SAP Gold partner and offer a wide bouquet of digitally integrated solutions ranging from implementing SAP-based ERP solutions like Rise with S/4HANA, Cloud-based solutions to 5D BIM for mega infra-projects, RPA & IoT solutions, and many more. With an in-depth understanding and core competence of the construction & infrastructure industry, Highbar is the perfect blend of domain knowledge in the infrastructure business with a team that is well equipped to understand and relate to the requirements of the sector.

To know more write us at enquiry@highbartech.com or call +91 89767 11399

[larebomrglobal]

Location Analytics Market Trends, Size, Competitive Analysis and Forecast - 2019-2025

The Location Analytics market is expected to grow at a CAGR of 14% over the forecast period 2019-2025. The Data Bridge Market Research Report for Fintech Blockchain Market provides analysis and insights on various factors that are expected to become widespread during the forecast period, while their impact on the market growth.

Location analysis refers to a business intelligence (BI) process that is applied to gain insights from geographic or location-based data. This allows businesses and organizations to merge 3D data with existing BI data across industries. 

Location Analytics Market is available at: https://www.orionmarketreports.com/location-analytics-market/37963/#ert_pane1-0

The major players covered in the location analytics market report are Google LLC, Precisely, IBM, SAS Institute Inc., Oracle, Microsoft, Tomtom International BV, HEXAGON, Zebra Technologies Corporation, Galigeo, Purple Innovation LLC, HERE, ALTERYX Inc., Indooratlas Ltd., CARTO, Tibco Software Inc., PLACEIQ, Ascend Cloud Solutions, Maplarge, Hardcastle GIS among other domestic and global players. Market share data is available for global, North America, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA) and South America separately. DBMR analysts understand competitive strengths and provide competitive analysis for each competitor separately. 

Get Sample Copy of Location Analytics Market : https://www.orionmarketreports.com/location-analytics-market/37963/#ert_pane1-1 

Location Analytics Market segmentation: 

By component:

Solutions

Services

By solution:

Geocoding and reverse geocoding

Data integration and ETL

Reporting and visualization

Thematic mapping and spatial analysis

Others (DBMS and socio-demographic data).

By service:

Consulting

Integration and deployment

Support and maintenance

By location type:

Outdoor location

Indoor location

By application:

Risk management

Emergency response management

Customer experience management

Remote monitoring

Supply chain planning and optimization

Sales and marketing optimization

Location selection and optimization

Others (predictive asset management and inventory management)

By vertical:

Retail

Manufacturing

Government and defense

Media and entertainment

Transportation and logistics

Energy and utilities

Telecom and it

Healthcare

Tourism and hospitality

Banking and financial services

Insurance

Real estate

Education

Recent Developments o Market Overview and growth analysis o Import and Export Overview o Volume Analysis o Current Market Trends and Future Outlook o Market Opportunistic and Attractive Investment Segment Geographic Coverage o North America Market Size and/or Volume o Latin America Market Size and/or Volume o Europe Market Size and/or Volume o Asia-Pacific Market Size and/or Volume o Rest of the world Market Size and/or Volume Key Questions Answered by Location Analytics Market Report

What was the Location Analytics Market size in 2018 and 2019; what are the estimated growth trends and market forecast (2019-2025).

What will be the CAGR of Location Analytics Market during the forecast period (2019-2025)?

Which segments (product type/applications/end-user) were most attractive for investments in 2018? How are these segments are expected to grow during the forecast period (2019-2025)?

Which manufacturer/vendor/players in the Location Analytics Market was the market leader in 2018?

Overview on the existing product portfolio, products in the pipeline, and strategic initiatives taken by key vendors in the market.

For more customized data, request for report customization @ https://www.orionmarketreports.com/location-analytics-market/37963/#ert_pane1-2

 About Us: Orion Market Reports (OMR) endeavors to provide exclusive blend of qualitative and quantitative market research reports to clients across the globe. Our organization helps both multinational and domestic enterprises to bolster their business by providing in-depth market insights and most reliable future market trends. Our reports address all the major aspects of the markets providing insights and market outlook to global clients.  

Media Contact: Company Name: Orion Market Reports Contact Person: Mr. Anurag Tiwari Email: info@orionmarketreports.com Contact no: +91 780-304-0404

[satpalda-blog]

GIS and Remote Sensing- Application in Pipeline Route Selection in Oil and Gas Industry

Route selection for optimal location is a foremost and crucial step in the overall pipeline design and construction process and also minimizing economic cost. GIS technologies can be used effectively in route determination process. Various thematic layers such as landslide, slope, geology, land use, soil, stream, lithology, road, culture and protected areas can be generated and integrated using RS-GIS techniques. ArcGIS Spatial Analysis module plays its part to convert these data layers into raster format to perform queries and analyses and finally obtain the shortest and the least cost path. In short, GIS-RS techniques can be deployed, integrating the human, environmental and financial factors, to obtain the best cost effective pipelines routes. 

 SATPALDA is a certified leading provider of geospatial products and services. The company is also a reseller of multiple satellite data products and has a proven track record of delivering project-critical geospatial products, including satellite imagery, UAV imagery, elevation models, LULC maps, Planimetry, terrain solutions. For more details, visit www.satpalda.com.

[annieboltonworld]

Juniper Publishers- Open Access Journal of Environmental Sciences & Natural Resources

Natural Resource Mapping Using Landsat and Lidar towards Identifying Digital Elevation, Digital Surface and Canopy Height Models

Authored by Abdul Qayum

Abstract

The evaluation of the natural resource plays a decisive role in management and sustainable developmental planning. The study aims to generate natural resource based maps of rural areas of the USA using remote sensing datasets such as Landsat and LiDAR to analyze generated natural resources for developing digital elevation (DEM) and canopy height models (CHMs). Natural resource maps were generated using Landsat image of the year 2010. The area statistics reveal that the tree class percent (23.21 %) was the second highest after fallow land class (60.44 %) whereas shrubs and grass/agriculture was 6.59 % and 6.47 %, respectively. Later, LiDAR datasets for the same time were used to generate DEM/surface models (DSM) and CHMs. Spatial analysis was done to attempt hypothetical queries pertaining to resource management. Further, all the generated datasets from different sources including slope map, drainage map etc can be integrated in GIS domain for effective decision making. Coarse resolution Landsat data can be effectively used to monitor natural resources up to micro level if used skillfully in conjugation with rectified images. LiDAR datasets have huge potential to generate highly accurate DEM, DSM and CHMs. Such accurate data will be informative for qualitative and quantitative assessment of various natural resources.

Keywords: Canopy Height model; DEM; DSM; Landsat; LiDAR; Natural Resource Map

Introduction

The natural resources signifying available land, water and forest have been serving the mankind by providing valuable services like food items, fuel and fodder, important medicines, regulating the air and water currents, protecting the soil and its components etc. FAO [1]. Moreover still the rural livelihood in every country is dependent on these natural resources and its condition for their sustainability and livelihood World Bank [2]. The major factors like population growth agriculture expansion and increased human well-being have been at the cost of forest depletion. The exponential increase in human population from seven billion to nine billion in the coming years has increased pressure on natural resources which are depleting at a faster rate than expected Millennium Ecosystem Assessment [3]. Further, the analysis of Ivanova et al. [4] has confirmed that some of the ecosystem services are at stake. We may soon face water scarcity, flooding of cities (because of degradation of wetlands) and increase in temperatures (because of overgrazing and overexploitation of forest trees).

Sustainable use of the natural resources would prevent further loss. As defined by Norwegian Ministry for the Environment [5] Sustainability means "the use of services and related products which respond to essential needs and ensure a better quality of life while minimizing the utilization of natural resources and toxic materials as well as control the emissions of waste and pollutants over the life-cycle so as not to jeopardize/ threaten the needs of future generations". Thus, a detailed management plan for the natural resource is needed which would identify clear objectives, evaluate existing natural resources and its uses to develop long term plans. To achieve this, a reliable geo-information database is required to prepare action plans for sustainable development at micro level. Natural resources assessment can be made using these parameters which would help the forest managers in multiple ways Hudak et al. [6]. These days satellite remote sensing data like LANDSAT, LiDAR etc are available for free download, which open up the opportunity for many researchers, government authority, and stakeholders for creation of data base on natural resources Kennaway et al. [7].

The advances in satellite remote sensing technology since few decades have been successful in providing data for detailed inventory and mapping of natural resources at large scale Kasturirangan et al. [8]. The applications of satellite remote sensing data have either been images from passive optical sensors for example aerial photography and Landsat Thematic Mapper Goward & Williams [9], or lesser known active radar sensors such as RADARSAT Waring et al. [10]. Light Detection and Ranging (LiDAR) has drawn tremendous attention among different natural resource managers and planners Hudak et al. [6]. LiDAR data provides the required resolution and detail of forests, rangelands, watersheds, roads, and other valued resources to improve management related decision supporting system Falkowski et al. [11]. LiDAR characterizations of ground and vegetation attributes are consistently accurate, highlighting its potential for various applications Deekshatulu [12]. LiDAR uses an active illumination sensor and can be collected in day or night. Besides, LiDAR data is free of any geometric distortions like side-looking radar. The capacity of LiDAR to capture multiple returns and to penetrate the ground even in dense forested areas opens the opportunity for the generation of a digital terrain model (DTM) and facilitates the estimation of variables Lim et al. [13], Hyyppa et al. [7]. Natural resource inventory at national or regional level can tap the potential of LiDAR data.

The advantages of LiDAR data over traditional remote sensing data is that data can be collected rapidly with greater accuracy (up to the centimeter), surface data has a higher sample density which helps in improving the results for various applications. In a forest, it can collect elevation data where photogrammetry fails to exhibit the accurate terrain surface due to the dense tree canopy cover. It adds another dimension that is "z" dimension to the spatial analysis and also increases the accuracy of biophysical parameters. Its sensors provide data regarding spatial distribution of plant canopies, sub canopy, vegetation height cover and canopy structure Lefsky et al. [14]. A Digital Surface Model (DSM) and the correspondent Digital Elevation Model (DEM) can be used in combination to retrieve by differencing the commonly known Canopy Height Model (CHM). It is a measure of the trees height over the area of interest as in case of woody vegetation or forest. CHM can be utilized to assess many fundamental forest parameters such as tree biomass and volume. Thus is gaining importance in identifying canopy height variability and detecting tree crowns Jakubowski et al. [15], Qin et al. [16].Other features pertaining to forest structure like canopy height, basal area, timber volume and biomass have all been successfully derived from LiDAR datasets Drake et al. [17], Drake et al. [18], Hyde et al. [19], Lefsky et al. [20], Magnussen & Boudewyn [21], Means et al. [22], Naesset [23], Nelson [24], Nilsson [25], Peterson [26], Asner et al. [27].

The LiDAR data can be used in conjunction with other open source data such as Google Earth. The pros of using Google Earth is that it provides freely available high resolution satellite data, which is useful for visual interpretation and delineation of various features Bey et al. [28], Malarvizhi et al. [29]. It also facilitates creation of vector layer for the area of interest and further exporting it in GIS domain can be used as a base map. It provides information (images of different time periods) for temporal analysis and one can also assess the history of a place Bey et al. [28]. All these provide insight about changes taking place with regard to natural resources. The cons of Google Earth are that one cannot obtain the original multispectral band data which limits the image classification analysis and quantification of data. The present study has attempted to generate base maps using a combination of Google Earth, Landsat and other thematic maps and to assess their feasibility to evaluate LiDAR dataset. The study area chosen was a rural area segment of the United States of America (USA) based upon the availability of both Landsat and LiDAR data. Different themes such as DSMs DEM and canopy height model (CHM) were generated using LiDAR dataset.

The Study Area

The study area was chosen based upon the availability of free download of LiDAR and Landsat remote sensing data for the same area of interest. It is located in rural USA, which is mostly dominated by trees, shrubs, wetlands (ponds, lakes, other water bodies) and agriculture. The geographical coordinates of the study area ranges from its Latitude 39°02'43.37"N to 39°08'47.71"N and Longitude 89°40'42.71"W to 89°49'07.83"W, whereas total geographical area is 136,81.4 Ha and its elevation ranges from 131 m to 236 m from the MSL (Figure 1).

Material and Methodology

Satellite data acquisition and preprocessing

The satellite remote sensing data of Landsat (April, 2010) and LiDAR (April, 2010) were downloaded from the USGS Earth explorer portal. The Landsat data was radio metrically and geometrically corrected and was devoid of cloud cover and line drop outs. The image processing, interpretation and delineation of different classes of natural resources was done in image processing software ERDAS Imagine, which is designed especially for remote sensing (Figure 2).

LiDAR data was analyzed in ArcGIS (developed by Environmental Systems Research Institute (ESRI)) platform, which is software used to create, display and analyze large geospatial data. Prior to accessing the images from the Google Earth, the study area shape file was created in ArcGIS and was divided into nine grids which were converted to *kmz file. This file was opened into Google Earth for visualizing. Each of the nine grids of Google Earth images were projected and mosaic ked in ERDAS Imagine. This mosaic ked file was used for creating the base map. The detailed schematic flowchart was formulated (Figure 2).

Image Processing and Classification

Landsat data was further subjected to image enhancement and classification to delineate various natural resource classes. For each class, (in false colour composite-FCC) some 10-15 clearly visible training sets were given, which were uniformly distributed throughout the scene. Supervised classification was performed using maximum likelihood algorithm. Seven natural resource classes namely trees, shrub, grassland/agriculture, settlement, water, current fallow and fallow were delineated in the study area. Anderson classification scheme (level I) was achieved by identifying seven natural resource categories in the Landsat image Anderson et al. [30].

Image accuracy assessment

Accuracy assessment determines the quality of the classified image. The accuracy assessment was executed by generating random points for each class in the classified dataset. The points were assigned to the respective classes based on the literature sources. The error matrix was generated and the overall classification accuracy and kappa statistics were calculated.

Base map preparation from the Google Earth

The study area vector (shape) file was divided into nine grids (3x3) in ArcGIS. The Shape file was converted into kmz file. This file was then opened in Google Earth containing all the nine grids. Each Google Earth image was rectified and mosaiced in ERDAS imagine. These mosaic images were taken as the base map. The advantage being that all features were distinct and can be visually interpreted and assist in further analysis.

LiDAR Data Analysis

LAS is the file format for LiDAR point clouds and the maiden step was to add LAS tiles into an LASD dataset, which was done in Arc Catalog by selecting NEW/LAS Dataset. By using LAS data set properties dialogue box, statistics can be created by clicking update button. This will provide a very useful overview on the point cloud data. The points can be viewed as Elevation values. Downloaded LiDAR data set were evaluated to run a quality assessment/ quality control (QA/QC) process on the data to ensure the data meets the delivery specifications. Such a process can also highlight the areas where the point density falls below a threshold level or data points falling below ground level, as well as the quality of the point classification.

All these aspects can affect the output data from any LiDAR processing undertaken. ArcGIS has quality control features for LiDAR point data within the LAS Point Statistics as Raster tool was used to evaluate the quality of data. The datasets in the study has no major issues as highlighted by the QA/QC process then the next step was to generate outputs in form of Digital Elevation Model (DEM) Digital Surface Model (DSM) Canopy Height Model (CHM), Contours and Slope etc. DEM and DSM can be created using LAS Dataset to Raster tool setting the DEM and DSM parameters; respectively. A critical element in natural resource management is the ability to measure tree heights. CHM is derived by subtracting the DEM from the DSM and can be done using the minus function of LIDAR Processing of Arc Toolbox.

Spatial Analysis over Datasets

Once different spatial data sets generated from data source rectified images can be used for spatial analysis quarries as they retain its geometry and projection. To understand this, an example may be taken as if a resource manager is interested in knowing the answer of following questions:

a. Simple query: Maps and area of trees growing on more than 10% slope; this slope is vulnerable due to its steepness towards erosion and needs special attention as regards soil and water conservation. This query needs two layers of natural resource maps and a slope map.

b. Complex query: Maps and area of trees growing in more than 10% slope and is vicinity of 300 m of road; this slope is more vulnerable due to its steepness towards soil erosion as well as accessibility of humans, high priority is required towards soil and water conservation practices. This query needs three layers of natural resource, slope and road buffer of 300 m maps.

Go to

Results and Discussion

Accuracy assessment of Natural resource map

The accuracy assessment was performed in classified images generated from Landsat images for the year 2010. The overall classification accuracy was 88% whereas kappa statistics was 0.80.

Base map preparation

The base maps provide information regarding various land features with considerable accuracy to give a lead to further analysis and interpretation. In this case, the downloaded, rectified and mosaic ked images of Google Earth acted as the base map as all the different features were quite distinct (Figure 3). Roads were digitized on it, which guided in supervised classification process for delineating various natural resource classes for the Landsat data (Figure 3).

Natural resource map class analysis

A natural resource map shows the expanse of natural resources found on and in the surface of the Earth. Based on the classification of Landsat data, natural resource map were generated for the study area, which was dominated by class trees, shrubs, water, grass/ agriculture, current fallow, fallow and settlement (Figure 4).

The Landsat TM data was visually interpreted based on the appearance of these classes in terms of colour and texture. They were used as signature (training sets) required for supervised classification of the dataset. A brief description of various features is given in (Table 1). The area and percent of each class was also calculated and the trees class percent was found to be the second highest after fallow land percent whereas shrub and grass/agriculture percent was 6.59 and 6.47, respectively (Table 2 & Figure 5).

LiDAR Data Analysis

The evaluation of QA/QC of LiDAR data was performed to check point density falling below a threshold level or not and it was found satisfactory (Figure 6).

DEM generation

In order to create DEM, the parameters need to be set on the LAS Dataset then to Raster tool. LAS require classification codes and returns to be defined. It varies depending on the product being derived. For a DEM, in the Layer Properties dialogue box Filter tab, the Classification Code should be set to 2, Ground, and the Returns set to 'ALL', and the DEM may be generated (Figure7). The contour (Figure-8) and slope map (Figure 9) were also generated based on this DEM (Figures 7-9).

DSM generation

Similar steps were followed to create DSM except that the LAS Dataset parameters in the Layer Properties dialogue box Filter tab were set to the classification code as set to 'All Classes', except 7 Noise and Returns set to 'First of Many; Single Return and Return 1'. Consequently, the DSM can be obtained (Figure 10).

Canopy Height Model generation

LiDAR technology has the ability to generate tree heights which is a critical element in natural resource management. Canopy model was derived by subtracting the DEM from the DSM (Figure 11). CHM gradient varies from 0 to 30 m was found (Figure 12). The area with respect to canopy height was also estimated (Figure 13). Furthermore, it was also estimated that the canopy height 22m occupied the maximum area (Figures 11-13).

Spatial Analysis over datasets

For answering queries, road buffer of 300 m was created from road map whereas slope map was grouped into two classes (one greater than 10% and other less than equal to 10%) and classified natural resource map was grouped into two classes (one tree class and other into other than tree class). The maps and area statistics was prepared based on spatial quarry as per the requirement from the resource manager (Figure 14).

Conclusion

The above study has shown that the prospects of mapping natural resource based upon Landsat images, LIDAR data sets and Google map in conjugation as reference map are better than the traditional remote sensing analysis. It has enhanced the mapping ability with more accuracy. The responses of LiDAR address a detailed measurement of all surfaces within a canopy volume which includes foliage, branches and stems. It has the capacity to accurately analyze the forest variables at landscape level and has been found to show more precise and cost effective measurements as compared to the traditional field related studies. At the local level that is at village level, such data can be harnessed for the betterment of rural people. This type of analysis can be incorporated in the making of the Natural Resources Management Plan (NRMP), which requires to the study the natural resource extent, problems and issues and recommending appropriate measures. Utilizing LiDAR data in an appropriate and judicious manner would open an opportunity for sustainable forest management practices by enhancing the existing knowledge and extending the expertise amongst the forest management community.

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[evoldir]

Fwd: Course: Crete.2.GISwithR_IntroENM.Nov20-29

Begin forwarded message: > From: evoldir@evol.biology.mcmaster.ca > Subject: Course: Crete.2.GISwithR_IntroENM.Nov20-29 > Date: 31 July 2019 at 18:29:52 GMT+12 > To: rdmpage@gmail.com > > > Dear evoldir members, > > Transmitting Science is offering the course 'GEOGRAPHICAL INFORMATION > SYSTEMS WITH R: USING R AS GIS (GREECE)'. > > Instructors: Dr. Neftalí Sillero (Centro de Investigaçao em Ciências > Geo-Espaciais Portugal) and Dr. A. Márcia Barbosa (CIBIO-InBIO, University > of Évora Portugal) > Dates: November 20th-22nd, 2019 > Location: Crete, Greece > > COURSE OVERVIEW: > > A Geographical Information System is a set of data, tools, software and > hardware, designed to store, manage, visualise, analyse, and represent > spatial data, either in vector (points, lines, polygons) or raster (image, > pixels) format. GIS is behind many activities of our world: from Google > Earth and Maps, GPS navigation management of logistic and transportation > services, land-cover maps such as CORINE, territorial planning, production > of topographical maps and other thematic cartographies, to natural risk > analyses and calculation of species' potential distributions. Every > activity using spatial data needs a GIS. > > Do you know how to work with spatial data? Do you need to use a > Geographic Information > Systems (GIS) but you cannot buy professional software? Do not worry, there > are many alternatives to freeware GIS that are at least as good as the > professional solutions. However, it is not so well-known that you can use > the famous programming language R as a GIS tool. > > This course will teach you the basic theory and practice of GIS using R. By > the end of this 3-day practical course, you will have the capacity to > visualise, map, and analyse spatial data, in vector as well as raster format > . > > This course is orientated to PhD and MSc students, as well as other > students and researchers working on any discipline using spatial data. > > This course will be mainly practical, with some theoretical lectures. All > spatial processes and analyses will be performed with R, which is free and > open-source software and allows automated and fully reproducible workflows. > Students will learn to import different types of spatial data, visualise > the data, perform spatial analyses, and represent the results in thematic > maps. > > PROGRAM: > Wednesday, November 20th, 2019. > > - Theory: Introduction to GIS. > - Practical: Introduction to GIS with R: Import and plot data. > - Theory: Cartography. > - Practical: Cartographic projections. > > Thursday, November 21st, 2019. > > - Theory: Vector G database base operations. > - Practical: Attribute and spatial queries: join/merge, filter/subset, > select by attribute, select by location, summarize, add/calculate new > attributes (columns), plot attributes. > - Theory: Vector analyses. > - P: Vector analyses: buffer, merge, dissolve, intersect, unite, select, > calculate the area. > > Friday, November 22nd, 2019. > > - Theory: Raster GIS. > - Practical: Raster analyses: rasterize, crop, mask, merge, distance > surface, zonal statistics. > > For more information and registration: http://bit.ly/GIS-with-R. > > Contact: courses.crete@transmittingscience.org > > All the best, > Haris Saslis, PhD > Course Coordinator > Transmitting Science > https://ift.tt/2ddjFfk > > haris.saslis@transmittingscience.org > > ----- > > Dear evoldir members, > > Transmitting Science is offering the course 'INTRODUCTION TO ECOLOGICAL > NICHE MODELLING'. > > Instructor: Dr. Neftalí Sillero (Centro de Investigaçao em Ciências > Geo-Espaciais Portugal) > Dates: November 25th-29th, 2019 > Location: Crete, Greece > > COURSE OVERVIEW: > > This course will teach you the base theory of ecological niche modelling > and its main methodologies. By the end of this 5-day practical course, you > will have the capacity to perform ecological niche models and understand > their results. You will be able to choose and apply the correct methodology > depending in the aim of your study and type of datasets. > > Ecological niche, species distribution, habitat distribution, or climatic > envelope models are different names for similar mechanistic or correlative > models, empirical or mathematical approaches to the ecological niche of a > species, where different types of ecogeographical variables (environmental, > topographical, human) are related with species physiological data or > geographical locations, in order to identify the factors limiting and > defining the species' niche. ENMs have become popular due to the need for > efficiency in the design and implementation of conservation management. > > The course will be mainly practical, with some theoretical lectures. All > modelling processes and calculations will be performed with R, the free > software environment for statistical computing and graphics ( > https://ift.tt/p6cJW1). Students will learn to use modelling algorithms > like Maxent, Bioclim, Domain, and logistic regressions, and R packages for > computing ENMs like Dismo and Biomod2. Also, students will learn to compare > different ecological niche models with Ecospat package. > > This course is oriented to PhD and MSc students, as well as other students > and researchers working on biogeography, spatial ecology, or related > disciplines. > > PROGRAM: > > Monday, November 25th, 2019. > > - Introduction to ENM theory. > - Problems on ENM + ENM methods. > - Conceptual and practice steps to calculate ENM. > - Applications of ENMs. > > Tuesday, November 26th, 2019. > > - Preparing variables. > - Dismo practice. > > Wednesday, November 27th, 2019. > > - Biomod practice. > - Maxent practice. > > Thursday, November 28th, 2019. > > - SDM practice. > - Students' talks. > > Friday, November 29th, 2019. > > - Final practice. > > For more information and registration: http://bit.ly/Intro-to-ENM. > > Contact: courses.crete@transmittingscience.org > > All the best, > Haris Saslis, PhD > Course Coordinator > Transmitting Science > https://ift.tt/2ddjFfk > > haris.saslis@transmittingscience.org > > > Haris Saslis > via IFTTT

[pinoyscientists]

Meet Julius Sempio:

1) What do you do?

I am an engineer and researcher with expertise in GIS-based satellite data processing-- satellite image processing and interpretation, spatial pattern analyses, thematic mapping, etc.-- and geospatial database design and management. I also handle some computer programming tasks that aid in completing the above-mentioned tasks efficiently, particularly in Python and Java.

2) Where do you work?

I am currently involved with the PHL-MICROSAT Program, more often referred to as the Diwata Program, as part of Project 3: Data Processing, Archiving and Distribution (DPAD).

3) Tell us about the photos!

[Top:] I gave an introductory talk on the Diwata Program and the prospects of the nation having its own space agency during the Geodetic Engineering Days of FEATI University in Quiapo, Manila. (Photos courtesy of FEATI SAGE)

[Bottom:] I am also involved in the arts! I was one of the Indie Tiangge exhibitors at November Komikon 2016, selling copies of my pet comics project titled “Tatlonghari”. So in case there’s going to be a comics event – at least in Metro Manila – expect my presence there either as an exhibitor or as a customer. (And in case you’re wondering, yes, I made the “diwata” drawing appearing on the “Top” photo collage. :)) (Photos courtesy of my elder sister)

4) Tell us about your academic career path so far. 

Elementary: Holy Spirit Academy of Malolos

High School: Philippine Science High School – Diliman

BS Geodetic Engineering: University of the Philippines – Diliman

Research Assistantships:

GIS Component: Pasig River Tributaries Survey and Assessment Study (Phase 2)

Watershed Modeling Component: Project Climate Twin Phoenix (Cagayan de Oro and Iligan)

MS Geomatics Engineering – Geoinformatics: University of the Philippines – Diliman

Research Associate: PHL-MICROSAT DPAD

5) Anything else you’d like to share?

I’d like to share three things:

1) Truth be told, in my undergraduate years I was a skeptic when it comes to a space program for the Philippines, pointing out other issues such as poverty and economic growth as more pressing concerns. But during my work at Cagayan de Oro and Iligan, I realized that the space program is itself a means to address poverty and economic growth because:

 Disaster preparedness will greatly reduce the impact of short-term poverty due to post-event consequences

Space-based monitoring aids in designing efficient management systems for industries such as agriculture, forestry, urban planning and even commerce

The Philippines is not going to be tied to foreign space services and won’t suffer in case such services are cut off

The space program itself provides new job opportunities

Others (please specify): __________________________________________

If you’d like some further reading, I’ll recommend this article as a spur: Why the Philippines Needs a National Space Agency (GMA News Online).

2) Almost everyone who would meet me and realize that I am an engineer would also be surprised to know that I am working on a comic book-- and dreaming of being a storyteller and concept artist for an animated series or even a video game-- saying that science and the arts are polar opposites. Personally, a scientist can also be an artist-- provided of course that he or she can provide ample time to work on both, as it does take dedication and a lot of willpower to achieve both. So when it comes to balancing your work with your passion, here is my advice: love both.

3) And if you want to read something to relax yourselves, go ahead and visit my comics project’s facebook page at fb.com/tatlonghariproject-- and if you did like it, please click on the “Like” button :)