In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

LANDISVIEW is a tool, developed at the Knowledge Engineering Laboratory at Texas A&M University, to visualize and animate 8-bit/16-bit ERDAS GIS format (e.g., LANDIS and LANDIS-II output maps). It can also convert 8-bit/16-bit ERDAS GIS format into ASCII and batch files. LANDISVIEW provides two major functions: 1) File Viewer: Files can be viewed sequentially and an output can be generated as a movie file or as an image file. 2) File converter: It will convert the loaded files for compatibility with 3rd party software, such as Fragstats, a widely used spatial analysis tool. Some available features of LANDISVIEW include: 1) Display cell coordinates and values. 2) Apply user-defined color palette to visualize files. 3) Save maps as pictures and animations as video files (*.avi). 4) Convert ERDAS files into ASCII grids for compatibility with Fragstats. (Source: http://kelab.tamu.edu/)

In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course. Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material. After completing this course you will be able to: prepare, manipulate, query, and generally work with data in R. perform data summarization, comparisons, and statistical tests. create quality graphs, map layouts, and interactive web maps to visualize data and findings. present your research, methods, results, and code as web pages to foster reproducible research. work with spatial data in R. analyze vector and raster geospatial data to answer a question with a spatial component. make spatial models and predictions using regression and machine learning. code in the R language at an intermediate level.

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GIS In Telecom Sector Market Size 2025-2029

The GIS in telecom sector market size is valued to increase USD 2.35 billion, at a CAGR of 15.7% from 2024 to 2029. Increased use of GIS for capacity planning will drive the GIS in telecom sector market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 28% growth during the forecast period.
By Product - Software segment was valued at USD 470.60 billion in 2023
By Deployment - On-premises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 256.91 million
Market Future Opportunities: USD 2350.30 million
CAGR from 2024 to 2029: 15.7%

Market Summary

The market is experiencing significant growth as communication companies increasingly adopt Geographic Information Systems (GIS) for network planning and optimization. Core technologies, such as satellite imagery and location-based services, are driving this trend, enabling telecom providers to improve network performance and customer experience. One major application of GIS in the telecom sector is capacity planning, which allows companies to optimize their network infrastructure based on real-time data.
However, the integration of GIS with big data and other advanced technologies presents a communication gap between developers and end-users, requiring a focus on user-friendly interfaces and training programs. Additionally, regulatory compliance and data security remain significant challenges for the market. Despite these hurdles, the opportunities for innovation and improved operational efficiency make the market an exciting and evolving space.

What will be the Size of the GIS In Telecom Sector Market during the forecast period?

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How is the GIS In Telecom Sector Market Segmented ?

The GIS in telecom sector industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Data
  Services


Deployment

  On-premises
  Cloud


Application

  Mapping
  Telematics and navigation
  Surveying
  Location based services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

The global telecom sector's reliance on Geographic Information Systems (GIS) continues to expand, with the market for GIS in telecoms projected to grow significantly. According to recent industry reports, the market for GIS data visualization and spatial data infrastructure in telecoms has experienced a notable increase of 18.7% in the past year. Furthermore, the demand for advanced spatial analysis tools, such as building penetration analysis, geospatial asset management, and work order management systems, has risen by 21.3%. Telecom companies utilize GIS for network performance monitoring, data integration platforms, and network planning. For instance, GIS enables network design, radio frequency interference analysis, route optimization software, mobile network optimization, signal propagation modeling, and service area mapping.

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The Software segment was valued at USD 470.60 billion in 2019 and showed a gradual increase during the forecast period.

Additionally, it plays a crucial role in infrastructure management, location-based services, emergency response planning, maintenance scheduling, and telecom network design. Moreover, the adoption of 3D GIS modeling, LIDAR data processing, and customer location mapping has gained traction, contributing to the market's expansion. The future outlook is promising, with industry experts anticipating a 25.6% increase in the use of GIS for telecom network capacity planning and telecom outage prediction. These trends underscore the continuous evolution of the market and its applications across various sectors.

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Regional Analysis

APAC is estimated to contribute 28% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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In China, the construction of smart cities in Qingdao, Hangzhou, and Xiamen, among others, is driving the demand for Geographic Information Systems (GIS) in various sectors. By 2025, China aims to build more smart cities, leading to significant growth opportunities for GIS companies. Esri Global Inc., a leading player

CanVec contains more than 60 topographic features classes organized into 8 themes: Transport Features, Administrative Features, Hydro Features, Land Features, Manmade Features, Elevation Features, Resource Management Features and Toponymic Features. This multiscale product originates from the best available geospatial data sources covering Canadian territory. It offers quality topographic information in vector format complying with international geomatics standards. CanVec can be used in Web Map Services (WMS) and geographic information systems (GIS) applications and used to produce thematic maps. Because of its many attributes, CanVec allows for extensive spatial analysis. Related Products: Constructions and Land Use in Canada - CanVec Series - Manmade Features Lakes, Rivers and Glaciers in Canada - CanVec Series - Hydrographic Features Administrative Boundaries in Canada - CanVec Series - Administrative Features Mines, Energy and Communication Networks in Canada - CanVec Series - Resources Management Features Wooded Areas, Saturated Soils and Landscape in Canada - CanVec Series - Land Features Transport Networks in Canada - CanVec Series - Transport Features Elevation in Canada - CanVec Series - Elevation Features Map Labels - CanVec Series - Toponymic Features

This data layer includes key performance metrics collected by the City and partners tracking the progress towards the goals of the Internet for All Seattle Initiative. Internet for All Seattle Dashboards. The data points reflect activities in five categories: 1) Affordable Connectivity Program, 2) Internet Connectivity, 3) Devices, 4) Digital Skills & Technical Support, and 5) Outreach & Assistance. The majority of the Internet for All Seattle Action Plan items and data fall under these five areas. Source data for Internet for All maps and dashboards.Updated quarterly. Last update: March 4, 2024. ATTRIBUTE NAME DEFINITION ADDITIONAL INFORMATION

Resource Organization or program providing metrics for this dashboard. Access for All Program - City of Seattle program to connect eligible organizations and locations in Seattle with free high speed internet service in partnership with Comcast, Astound Broadband, and Lumen. City of Seattle Facilities - City owned buildings, including Community Centers, City Hall, Seattle Center and others. Internet Essentials Program - Low-cost internet program provided by Comcast offering $9.95/month + tax for eligible households. Internet First Program - Low-cost internet program provided by Astound offering $50 Mbps Internet* to qualifying low-income households. Other Partners - Other organizations partnering with the City of Seattle. Seattle Housing Authority - An independent public corporation in the city of Seattle responsible for public housing for low-income, elderly, and disabled residents. Seattle IT Digital Equity - City of Seattle, Seattle Information Technology Department Digital Equity Program. Seattle IT Digital Navigator - Seattle IT grant program providing funding to community-based organizations to provide digital navigation services. Seattle IT Technology Matching Fund - City of Seattle grant program providing funding to community-based organizations to increase internet access and adoption. Seattle Public Library - The public library system serving the city of Seattle Seattle Public Schools - The public school district serving the city of Seattle. Simply Internet Program - Low-cost internet program provided by Astound offering for $9.95/month + tax for eligible households.

Location_Name Additional info about physical location.

Organization Nonprofit or community group funded by the City.

Project_Title Title of a project funded by the City.

Budget Budget value associated with a resource.

Date Date metrics were reported.

Award_Year Year a grant was awarded to a grantee.

Street_Address Address of physical location.

City City of physical location.

State State of physical location.

ZIP ZIP of physical location.

Council_District Council District resource is located in.

Longitude Longitude of physical location.

Latitude Latitude of physical location.

ISP An organization that provides services for accessing, using, or participating in the Internet.

Citywide_Y_N Is resource provided throughout City.

Devices_Distributed The number of devices that were provided to residents.

Devices_Distributed_Y_N Is there a value in Devices_Distributed field (used to create dashboards).

Devices_Loaned The number of devices that were loaned to residents for temporary use.

Devices_Loaned_Y_N Is there a value in Devices_Loaned field (used to create dashboards).

DSTS_TotalServed The number of residents served by digital skills training and technical support programs. DSTS refers to Digital Skills and Training Support

DSTS_TotalServed_Y_N Is there a value in DSTS_TotalServed field (used to create dashboards).

DSTS_Hours The number of hours of digital skills training and technical support provided.

DSTS_Hours_Y_N Is there a value in DSTS_Hours field (used to create dashboards).

IC_Hotspots_Sponsored Number of residents provided with hotspots or sponsored internet service. IC refers to Internet Connectivity

IC_Hotspots_Sponsored_Y_N Is there a value in IC_Hotspots_Sponsored field (used to create dashboards).

IC_PubWiFiConnections Number of Wi-Fi connections provided at public Wi-Fi sites.

IC_PubWiFiConnections_Y_N Is there a value in IC_PubWiFiConnections field (used to create dashboards).

IC_PubWiFiSites Number of sites providing public Wi-Fi.

IC_PubWiFiSites_Y_N Is there a value in IC_PubWiFiSites field (used to create dashboards).

IC_LowCostServices The number of residents enrolled in Low-cost internet programs offered by Comcast and Astound.

IC_LowCostServices_Y_N Is there a value in IC_LowCostServices field (used to create dashboards).

IC_Organizations Sites providing internet connectivity through their organization. Federal Subsidy Program Emergency Broadband Program (EBB) was a federal program to help low-income households afford broadband services and internet-connected devices during the pandemic. The program officially ended in early 2022 and was replaced by the Affordable Connectivity Program. The Affordable Connectivity Program (ACP) is a federal program to help low-income households afford broadband services and internet-connected devices during the pandemic. The Program provides a discount of up to $30 per month for broadband services for eligible consumers.

IC_Organizations_Y_N Is there a value in IC_Organizations field (used to create dashboards).

IC_FedSubsEBBACP Number of total households that participated in the EBB or ACP programs.

IC_FedSubsEBBACP_Y_N Is there a value in IC_FedSubsEBBACP field (used to create dashboards).

OA_InternetServReqs The number of requests from the public for information about internet service. These requests come to the City and are fulfilled by Seattle IT Digital Equity staff. OA refers to Outreach and Assistance

OA_InternetServReqs_Y_N Is there a value in OA_InternetServReqs field (used to create dashboards).

OA_LowInternetInfo The number of requests from the public for information about low-income internet service. These requests come to the City and are fulfilled by Seattle IT Digital Equity staff.

OA_LowInternetInfo_Y_N Is there a value in OA_LowInternetInfo field (used to create dashboards).

OA_LowInternetevent Number of residents provided with information about free or low-cost internet at outreach events. This outreach is conducted by Seattle IT Digital Equity staff.

OA_LowInternetevent_Y_N Is there a value in OA_LowInternetevent field (used to create dashboards)

Download and use the 250m True Marble global dataset for free! This is a low resolution version of our full 15m product, but it is quite useful. Download to use on your web page or preview a purchase. We only ask that you display our copyright and reference this page when using it. Two types of files are available for download: GeoTIFF and PNG. The GeoTIFF files are better suited for GIS programs, but are generally a larger file size. The PNG files are for general image processing programs, but are not georeferenced. Most of these files are much too large for your web browser to display, so be sure to save the file directly to disk. ![]() ![]() ![]() ![]() ![](

The "Map Imager Layer - Administrative Boundaries" is a Map Image Layer of Administrative Boundaries. It has been designed specifically for use in ArcGIS Online (and will not directly work in ArcMap or ArcPro). This data has been modified from the original source data to serve a specific business purpose. This data is for cartographic purposes only.The Administrative Boundaries Data Group contains the following layers: Populated Places (USGS)US Census Urbanized Areas and Urban Clusters (USCB)US Census Minor Civil Divisions (USCB)PLSS Townships (MnDNR, MnGeo)Counties (USCB)American Indian, Alaska Native, Native Hawaiian (AIANNH) Areas (USCB)States (USCB)Countries (MPCA)These datasets have not been optimized for fast display (but rather they maintain their original shape/precision), therefore it is recommend that filtering is used to show only the features of interest. For more information about using filters please see "Work with map layers: Apply Filters": https://doc.arcgis.com/en/arcgis-online/create-maps/apply-filters.htmFor additional information about the Administrative Boundary Dataset please see:United States Census Bureau TIGER/Line Shapefiles and TIGER/Line Files Technical Documentation: https://www.census.gov/programs-surveys/geography/technical-documentation/complete-technical-documentation/tiger-geo-line.htmlUnited States Census Bureau Census Mapping Files: https://www.census.gov/geographies/mapping-files.htmlUnited States Census Bureau TIGER/Line Shapefiles: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html and https://www.census.gov/cgi-bin/geo/shapefiles/index.php

Background: Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania, for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.

Methods: This study employs a geospatial-based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability. Results: The study demonstrates that the majority of the study area falls under the moderate-risk level (61%), followed by the low-risk level (31%), while the high-malaria risk area covers a small area, which occupies only 8% of the total area. Conclusion: The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions. Methods Data acquisition and description The study employed both primary and secondary data, which were collected from numerous sources based on the input required for the implementation of the predictive model. Data collected includes the locations of all public and private health centers that were downloaded free from the health portal of the United Republic of Tanzania, Ministry of Health, Community Development, Gender, Elderly, and Children, through the universal resource locator (URL) (http://moh.go.tz/hfrportal/). Human population data was collected from the 2012 population housing census (PHC) for the United Republic of Tanzania report. Rainfall data were obtained from two local offices; Kilombero Agricultural Training and Research Institute (KATRIN) and Kilombero Valley Teak Company (KVTC). These offices collect meteorological data for agricultural purposes. Monthly data from 2012 to 2017 provided from thirteen (13) weather stations. Road and stream network shapefiles were downloaded free from the MapCruzin website via URL (https://mapcruzin.com/free-tanzania-arcgis-maps-shapefiles.htm). With respect to the size of the study area, five neighboring scenes of the Landsat 8 OLI/TIRS images (path/row: 167/65, 167/66, 167/67, 168/66 and 168/67) were downloaded freely from the United States Geological Survey (USGS) website via URL: http://earthexplorer.usgs.gov. From July to November 2017, the images were selected and downloaded from the USGS Earth Explorer archive based on the lowest amount of cloud cover coverage as viewed from the archive before downloading. Finally, the digital elevation data with a spatial resolution of three arc-seconds (90m by 90m) using WGS 84 datum and the Geographic Coordinate System were downloaded free from the Shuttle Radar Topography Mission (SRTM) via URL (https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Africa/). Only six tiles that fall in the study area were downloaded, coded tiles as S08E035, S09E035, S10E035, S08E036, S09E036, S10E036, S08E037, S09E037 and S10E037. Preparation and Creation of Model Factor Parameters Creation of Elevation Factor All six coded tiles were imported into the GIS environment for further analysis. Data management tools, with raster/raster data set/mosaic to new raster feature, were used to join the tiles and form an elevation map layer. Using the spatial analyst tool/reclassify feature, the generated elevation map was then classified into five classes as 109–358, 359–530, 531–747, 748–1017 and >1018 m.a.s.l. and new values were assigned for each class as 1, 2, 3, 4 and 5, respectively, with regards to the relationship with mosquito distribution and malaria risk. Finally, the elevation map based on malaria risk level is levelled as very high, high, moderate, low and very low respectively. Creation of Slope Factor A slope map was created from the generated elevation map layer, using a spatial analysis tool/surface/slope feature. Also, the slope raster layer was further reclassified into five subgroups based on predefined slope classes using standard classification schemes, namely quantiles as 0–0.58, 0.59–2.90, 2.91–6.40, 6.41–14.54 and >14.54. This classification scheme divides the range of attribute values into equal-sized sub-ranges, which allow specifying the number of the intervals while the system determines where the breaks should be. The reclassified slope raster layer subgroups were ranked 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence in the locality. To elaborate, the steeper slope values are related to lesser malaria hazards, and the gentler slopes are highly susceptible to malaria incidences. Finally, the slope map based on malaria risk level is leveled as very high, high, moderate, low and very low respectively. Creation of Curvature Factor Curvature is another topographical factor that was created from the generated elevation map using the spatial analysis tool/surface/curvature feature. The curvature raster layer was further reclassified into five subgroups based on predefined curvature class. The reclassified curvature raster layer subgroups were ranked to 1, 2, 3, 4 and 5 according to their degree of suitability for malaria occurrence. To explain, this affects the acceleration and deceleration of flow across the surface. A negative value indicates that the surface is upwardly convex, and flow will be decelerated, which is related to being highly susceptible to malaria incidences. A positive profile indicates that the surface is upwardly concave and the flow will be accelerated which is related to a lesser malaria hazard, while a value of zero indicates that the surface is linear and related to a moderate malaria hazard. Lastly, the curvature map based on malaria risk level is leveled as very high, high, moderate, low, and very low respectively.
Creation of Aspect Factor As a topographic factor associated with mosquito larval habitat formation, aspect determines the amount of sunlight an area receives. The more sunlight received the stronger the influence on temperature, which may affect mosquito larval survival. The aspect of the study area also was generated from the elevation map using spatial analyst tools/ raster /surface /aspect feature. The aspect raster layer was further reclassified into five subgroups based on predefined aspect class. The reclassified aspect raster layer subgroups were ranked as 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence, and new values were re-assigned in order of malaria hazard rating. Finally, the aspect map based on malaria risk level is leveled as very high, high, moderate, low, and very low, respectively. Creation of Human Population Distribution Factor Human population data was used to generate a population distribution map related to malaria occurrence. Kilombero Valley has a total of 42 wards, the data was organized in Ms excel 2016 and imported into the GIS environment for the analysis, Inverse Distance Weighted (IDW) interpolation in the spatial analyst tool was applied to interpolate the population distribution map. The population distribution map was further reclassified into five subgroups based on potential to malaria risk. The reclassified map layer subgroups were ranked according to the vulnerability to malaria incidence in the locality such as areas having high population having the highest vulnerability and the less population having less vulnerable, and the new value was assigned as 1, 2, 3, 4 and 5, and then leveled as very high, high, moderate, low and very low malaria risk level, respectively. Creation of Proximity to Health Facilities Factor The distribution of health facilities has a significant impact on the malaria vulnerability of the population dwellings in the Kilombero Valley. The health facility layer was created by computing distance analysis using proximity multiple ring buffer features in spatial analyst tool/multiple ring buffer. Then the map layer was reclassified into five sub-layers such as within (0–5) km, (5.1–10) km, (10.1–20) km, (20.1–50) km and >50km. According to a WHO report, it is indicated that the human population who live nearby or easily accessible to health facilities is less vulnerable to malaria incidence than the ones who are very far from the health facilities due to the distance limitation for the health services. Later on, the new values were assigned as 1, 2, 3, 4 and 5, and then reclassified as very high, high, moderate, low and very low malaria risk levels, respectively. Creation of Proximity to Road Network Factor The distance to the road network is also a significant factor, as it can be used as an estimation of the access to present healthcare facilities in the area. Buffer zones were calculated on the path of the road to determine the effect of the road on malaria prevalence. The road shapefile of the study area was inputted into GIS environment and spatial analyst tools / multiple ring buffer feature were used to generate five buffer zones with the

Conservation Biology Field Courses Market Outlook

According to our latest research, the global conservation biology field courses market size in 2024 stands at USD 1.42 billion, reflecting the expanding emphasis on environmental education and field-based learning worldwide. The market is experiencing a robust growth trajectory, with a compound annual growth rate (CAGR) of 7.3% projected from 2025 to 2033. By the end of 2033, the market is expected to reach USD 2.68 billion. This notable growth is primarily driven by increasing demand for experiential learning, the critical need for biodiversity conservation, and the integration of technology in field education.

One of the primary growth factors for the conservation biology field courses market is the rising global awareness about biodiversity loss and climate change. As environmental challenges become more complex and urgent, educational institutions, NGOs, and governmental agencies are prioritizing hands-on learning experiences that equip participants with practical conservation skills. This shift toward field-based education is further supported by international frameworks such as the United Nations’ Sustainable Development Goals (SDGs), which emphasize the importance of education in achieving environmental sustainability. Consequently, both undergraduate and graduate programs are increasingly incorporating field courses into their curricula, resulting in heightened enrollment rates and expanding market opportunities.

Another significant driver is the evolution of pedagogical approaches in conservation science. There is a growing recognition that classroom-based theoretical instruction alone is insufficient to address real-world conservation challenges. Field courses provide immersive experiences that foster critical thinking, problem-solving, and collaboration among participants. This educational transformation is not limited to universities; professional development programs and short-term workshops are also gaining traction among early-career scientists, conservation practitioners, and policy makers. The adoption of hybrid and online delivery modes has further democratized access, enabling participants from remote or underserved regions to engage in high-quality field-based learning.

Technological advancements also play a pivotal role in shaping the conservation biology field courses market. The integration of digital tools such as GIS mapping, remote sensing, and mobile data collection platforms has revolutionized fieldwork, making it more efficient and data-driven. These innovations enhance the learning experience, allowing students and professionals to analyze complex ecological data in real time and contribute meaningfully to ongoing conservation projects. Moreover, partnerships between academic institutions, research organizations, and technology providers are fostering the development of cutting-edge curricula that address current and emerging conservation issues, further fueling market growth.

From a regional perspective, North America and Europe currently dominate the conservation biology field courses market, accounting for over 60% of the global market share in 2024. These regions benefit from well-established educational infrastructures, strong funding support, and a mature ecosystem of conservation organizations. However, the Asia Pacific region is emerging as a significant growth engine, driven by rapid biodiversity loss, increasing governmental investment in environmental education, and the expansion of international collaborations. Latin America and the Middle East & Africa are also witnessing rising interest, particularly in areas with high conservation value and pressing ecological challenges. This regional diversity presents unique opportunities for market players to tailor their offerings to local needs and contexts.

Course Type Analysis

The course type segment in the conservation biology field courses market is broadly categorized into undergraduate, graduate, professional development, and short-te

GIS Retrofit Monitoring Sensors Market Outlook

According to our latest research, the GIS Retrofit Monitoring Sensors market size reached USD 1.27 billion globally in 2024, driven by the surge in grid modernization and the growing emphasis on asset management in power infrastructure. The market is projected to grow at a robust CAGR of 8.5% from 2025 to 2033, with the total value expected to reach USD 2.62 billion by 2033. The rapid adoption of smart grid technologies, increasing investments in power transmission and distribution networks, and the need for real-time monitoring of Gas Insulated Switchgear (GIS) are the primary growth factors fueling this market’s expansion.

A significant growth driver in the GIS Retrofit Monitoring Sensors market is the global trend toward grid digitalization and automation. Utilities and power operators are increasingly adopting advanced monitoring solutions to enhance the reliability and efficiency of electrical grids. The integration of Internet of Things (IoT) and artificial intelligence (AI) into GIS monitoring systems has enabled real-time data collection and predictive analytics, reducing downtime and operational costs. This digital transformation not only allows for proactive maintenance but also extends the operational life of critical GIS assets, making retrofit monitoring sensors indispensable for modern energy infrastructure. The demand for retrofit solutions is particularly high because they enable utilities to upgrade existing GIS systems without the need for costly replacements, ensuring compliance with stringent regulatory standards and improving asset performance.

Another key factor propelling the GIS Retrofit Monitoring Sensors market is the increasing focus on safety, reliability, and environmental sustainability in power systems. Gas Insulated Switchgear, while compact and efficient, poses risks related to insulation failure, gas leaks, and overheating. Retrofit monitoring sensors, such as partial discharge, gas, and temperature sensors, provide early detection of faults and anomalies, enabling operators to implement timely interventions and avoid catastrophic failures. Furthermore, the growing adoption of renewable energy sources and distributed generation has intensified the need for real-time condition monitoring to manage fluctuating loads and maintain grid stability. As governments and regulatory bodies enforce stricter safety and environmental regulations, the deployment of advanced monitoring sensors in GIS installations has become a strategic priority for utilities and industrial end-users.

The surge in infrastructure investments across emerging economies is also contributing to the expansion of the GIS Retrofit Monitoring Sensors market. Countries in Asia Pacific, Latin America, and the Middle East are witnessing rapid urbanization and industrialization, necessitating upgrades to aging power infrastructure and the deployment of reliable monitoring systems. The need to minimize transmission losses, improve energy efficiency, and ensure uninterrupted power supply is driving utilities and industrial players to invest in retrofit monitoring solutions. Additionally, the increasing incidence of extreme weather events and grid disturbances has underscored the importance of resilient and intelligent monitoring technologies, further accelerating market growth in these regions.

Regionally, Asia Pacific dominates the GIS Retrofit Monitoring Sensors market, accounting for the largest share in 2024, followed by North America and Europe. The region’s leadership is attributed to large-scale grid expansion projects, rapid adoption of smart grid technologies, and substantial investments in renewable energy integration. North America and Europe are also witnessing steady growth due to ongoing grid modernization initiatives, regulatory mandates for grid reliability, and the presence of leading technology providers. Meanwhile, emerging markets in Latin America and the Middle East & Africa are poised for significant growth, supported by infrastructure development programs and increasing focus on energy efficiency.

Measure the distance between two rain gauges to estimate how much precipitation an intervening town receives by deriving a linear function. THE GEOINQUIRIES™ COLLECTION FOR MATHEMATICShttp://www.esri.com/geoinquiriesThe GeoInquiry™ collection for Mathematics contains 15 free, standards-based activities that correspond and extend spatial concepts found in course textbooks frequently used in introductory algebra or geometry classes. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the Common Core math national curriculum standards. Activities include:· Rates & Proportions: A lost beach· D=R x T· Linear rate of change: Steady growth· How much rain? Linear equations· Rates of population change· Distance and midpoint· The coordinate plane· Euclidean vs Non-Euclidean· Area and perimeter at the mall· Measuring crop circles· Area of complex figures· Similar triangles· Perpendicular bisectors· Centers of triangles· Volume of pyramids

Teachers, GeoMentors, and school administrators can learn more at http://www.esri.com/geoinquiries.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

This dataset was sourced from the Queensland Department of Natural Resources and Mines in 2012. Information provided by the Department describes the dataset as follows:

This data was originally provided on DVD and contains the converted shapefiles, layer files, raster images and project .mxd files used on the Queensland geology and structural framework map. The maps were done in ArcGIS 9.3.1 and the data stored in file geodatabases, topology created and validated. This provides greater data quality by performing topological validation on the feature's spatial relationships. For the purposes of the DVD, shapefiles were created from the file geodatabases and for MapInfo users MapInfo .tab and .wor files. The shapefiles on the DVD are a revision of the 1975 Queensland geology data, and are both are available for display, query and download on the department's online GIS application.

The Queensland geology map is a digital representation of the distribution or extent of geological units within Queensland. In the GIS, polygons have a range of attributes including unit name, type of unit, age, lithological description, dominant rock type, and an abbreviated symbol for use in labelling the polygons. The lines in this dataset are a digital representation of the position of the boundaries of geological units and other linear features such as faults and folds. The lines are attributed with a description of the type of line represented. Approximately 2000 rock units were grouped into the 250 map units in this data set. The digital data was generalised and simplified from the Department's detailed geological data and was captured at 1:500 000 scale for output at 1:2 000 000 scale.

In the ESRI version, a layer file is provided which presents the units in the colours and patterns used on the printed hard copy map. For Map Info users, a simplified colour palette is provided without patterns. However a georeferenced image of the hard copy map is included and can be displayed as a background in both Arc Map and Map Info.

The geological framework of Queensland is classified by structural or tectonic unit (provinces and basins) in which the rocks formed. These are referred to as basins (or in some cases troughs and depressions) where the original form and structure are still apparent. Provinces (and subprovinces) are generally older basins that have been strongly tectonised and/or metamorphosed so that the original basin extent and form are no longer preserved. Note that intrusive and some related volcanic rocks that overlap these provinces and basins have not been included in this classification. The map was compiled using boundaries modified and generalised from the 1:2 000 000 Queensland Geology map (2012). Outlines of subsurface basins are also shown and these are based on data and published interpretations from petroleum exploration and geophysical surveys (seismic, gravity and magnetics).

For the structural framework dataset, two versions are provided. In QLD_STRUCTURAL_FRAMEWORK, polygons are tagged with the name of the surface structural unit, and names of underlying units are imbedded in a text string in the HIERARCHY field. In QLD_STRUCTURAL_FRAMEWORK_MULTI_POLYS, the data is structured into a series of overlapping, multi-part polygons, one for each structural unit. Two layer files are provided with the ESRI data, one where units are symbolised by name. Because the dataset has been designed for units display in the order of superposition, this layer file assigns colours to the units that occur at the surface with concealed units being left uncoloured. Another layer file symbolises them by the orogen of which they are part. A similar set of palettes has been provided for Map Info.

Dataset History

Details on the source data can be found in the xml file associated with data layer.

Data in this release

*ESRI.shp and MapInfo .tab files of rock unit polygons and lines with associated layer attributes of Queensland geology

*ESRI.shp and MapInfo .tab files of structural unit polygons and lines with associated layer attributes of structural framework

*ArcMap .mxd and .lyr files and MapInfo .wor files containing symbology

*Georeferenced Queensland geology map, gravity and magnetic images

*Queensland geology map, structural framework and schematic diagram PDF files

*Data supplied in geographical coordinates (latitude/longitude) based on Geocentric Datum of Australia - GDA94

Accessing the data

Programs exist for the viewing and manipulation of the digital spatial data contained on this DVD. Accessing the digital datasets will require GIS software. The following GIS viewers can be downloaded from the internet. ESRI ArcExplorer can be found by a search of www.esriaustralia.com.au and MapInfo ProViewer by a search on www.pbinsight.com.au collectively ("the websites").

Metadata

Metadata is contained in .htm files placed in the root folder of each vector data folder. For ArcMap users metadata for viewing in ArcCatalog is held in an .xml file with each shapefile within the ESRI Shapefile folders.

Disclaimer

The State of Queensland is not responsible for the privacy practices or the content of the websites and makes no statements, representations, or warranties about the content or accuracy or completeness of, any information or products contained on the websites.

Despite our best efforts, the State of Queensland makes no warranties that the information or products available on the websites are free from infection by computer viruses or other contamination.

The State of Queensland disclaims all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages and costs you might incur as a result of accessing the websites or using the products available on the websites in any way, and for any reason.

The State of Queensland has included the websites in this document as an information source only. The State of Queensland does not promote or endorse the websites or the programs contained on them in any way.

WARNING: The Queensland Government and the Department of Natural Resources and Mines accept no liability for and give no undertakings, guarantees or warranties concerning the accuracy, completeness or fitness for the purposes of the information provided. The consumer must take all responsible steps to protect the data from unauthorised use, reproduction, distribution or publication by other parties.

Please view the 'readme.html' and 'licence.html' file for further, more complete information

Dataset Citation

Geological Survey of Queensland (2012) Queensland geology and structural framework - GIS data July 2012. Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/69da6301-04c1-4993-93c1-4673f3e22762.

an index map for the 62 QuickBird tiles (ESRI Shapefile format)

Mineral resource occurrence data covering the world, most thoroughly within the U.S. This database contains the records previously provided in the Mineral Resource Data System (MRDS) of USGS and the Mineral Availability System/Mineral Industry Locator System (MAS/MILS) originated in the U.S. Bureau of Mines, which is now part of USGS. The MRDS is a large and complex relational database developed over several decades by hundreds of researchers and reporters. While database records describe mineral resources worldwide, the compilation of information was intended to cover the United States completely, and its coverage of resources in other countries is incomplete. The content of MRDS records was drawn from reports previously published or made available to USGS researchers. Some of those original source materials are no longer available. The information contained in MRDS was intended to reflect the reports used as sources and is current only as of the date of those source reports. Consequently MRDS does not reflect up-to-date changes to the operating status of mines, ownership, land status, production figures and estimates of reserves and resources, or the nature, size, and extent of workings. Information on the geological characteristics of the mineral resource are likely to remain correct, but aspects involving human activity are likely to be out of date.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple the Queensland geology and structural framework dataset. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This dataset contains a polygon shapefile of the Belyando Basin province boundary. The Belyando Basin underlies the eastern margin of the Galilee subregion. Extracted from the QLD Geology and Structural Framework of 2012 - the abstract of which is below.

The data on this DVD contains the converted shapefiles, layer files, raster images and project .mxd files used on the Queensland geology and structural framework map. The maps were done in ArcGIS 9.3.1 and the data stored in file geodatabases, topology created and validated. This provides greater data quality by performing topological validation on the feature's spatial relationships. For the purposes of the DVD, shapefiles were created from the file geodatabases and for MapInfo users MapInfo .tab and .wor files. The shapefiles on the DVD are a revision of the 1975 Queensland geology data, and are both are available for display, query and download on the department's online GIS application.

The Queensland geology map is a digital representation of the distribution or extent of geological units within Queensland. In the GIS, polygons have a range of attributes including unit name, type of unit, age, lithological description, dominant rock type, and an abbreviated symbol for use in labelling the polygons. The lines in this dataset are a digital representation of the position of the boundaries of geological units and other linear features such as faults and folds. The lines are attributed with a description of the type of line represented. Approximately 2000 rock units were grouped into the 250 map units in this data set. The digital data was generalised and simplified from the Department's detailed geological data and was captured at 1:500 000 scale for output at 1:2 000 000 scale.

The geological framework of Queensland is classified by structural or tectonic unit (provinces and basins) in which the rocks formed. These are referred to as basins (or in some cases troughs and depressions) where the original form and structure are still apparent. Provinces (and subprovinces) are generally older basins that have been strongly tectonised and/or metamorphosed so that the original basin extent and form are no longer preserved. Note that intrusive and some related volcanic rocks that overlap these provinces and basins have not been included in this classification. The map was compiled using boundaries modified and generalised from the 1:2 000 000 Queensland Geology map (2012). Outlines of subsurface basins are also shown and these are based on data and published interpretations from petroleum exploration and geophysical surveys (seismic, gravity and magnetics).

For the structural framework dataset, two versions are provided. In QLD_STRUCTURAL_FRAMEWORK, polygons are tagged with the name of the surface structural unit, and names of underlying units are imbedded in a text string in the HIERARCHY field. In QLD_STRUCTURAL_FRAMEWORK_MULTI_POLYS, the data is structured into a series of overlapping, multi-part polygons, one for each structural unit. Two layer files are provided with the ESRI data, one where units are symbolised by name. Because the dataset has been designed for units display in the order of superposition, this layer file assigns colours to the units that occur at the surface with concealed units being left uncoloured. Another layer file symbolises them by the orogen of which they are part. A similar set of palettes has been provided for Map Info.

Purpose

This dataset provides a single, merged representation of the Belyando Basin as interpreted by the QLD Geology and Structural Framework of 2012

Dataset History

This dataset has been extracted directly from the QLD Geology and Structural Framework: QLD_STRUCTURAL_FRAMEWORK.shp.

1. Features with the following 'Heirarchy' attributes were selected and extracted:

a) Galilee Basin>Drummond Basin>Belyando Basin>Thomson Orogen

b) Eromanga Basin>Galilee Basin>Drummond Basin>Belyando Basin>Thomson Orogen

c) Drummond Basin>Belyando Basin>Thomson Orogen

d) Galilee Basin>Drummond Basin>Belyando Basin>Thomson Orogen

1. Features were merged together to produce the Belyando Basin province.

The lineage of the QLD Geology and Structural Framework is below:

Data in this release

*ESRI.shp and MapInfo .tab files of rock unit polygons and lines with associated layer attributes of Queensland geology

*ESRI.shp and MapInfo .tab files of structural unit polygons and lines with associated layer attributes of structural framework

*ArcMap .mxd and .lyr files and MapInfo .wor files containing symbology

*Georeferenced Queensland geology map, gravity and magnetic images

*Queensland geology map, structural framework and schematic diagram PDF files

*Data supplied in geographical coordinates (latitude/longitude) based on Geocentric Datum of Australia - GDA94

Accessing the data

Programs exist for the viewing and manipulation of the digital spatial data contained on this DVD. Accessing the digital datasets will require GIS software. The following GIS viewers can be downloaded from the internet. ESRI ArcExplorer can be found by a search of www.esriaustralia.com.au and MapInfo ProViewer by a search on www.pbinsight.com.au collectively ("the websites").

Metadata

Metadata is contained in .htm files placed in the root folder of each vector data folder. For ArcMap users metadata for viewing in ArcCatalog is held in an .xml file with each shapefile within the ESRI Shapefile folders.

Disclaimer

The State of Queensland is not responsible for the privacy practices or the content of the websites and makes no statements, representations, or warranties about the content or accuracy or completeness of, any information or products contained on the websites.

Despite our best efforts, the State of Queensland makes no warranties that the information or products available on the websites are free from infection by computer viruses or other contamination.

The State of Queensland disclaims all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages and costs you might incur as a result of accessing the websites or using the products available on the websites in any way, and for any reason.

The State of Queensland has included the websites in this document as an information source only. The State of Queensland does not promote or endorse the websites or the programs contained on them in any way.

WARNING: The Queensland Government and the Department of Natural Resources and Mines accept no liability for and give no undertakings, guarantees or warranties concerning the accuracy, completeness or fitness for the purposes of the information provided. The consumer must take all responsible steps to protect the data from unauthorised use, reproduction, distribution or publication by other parties.

Dataset Citation

Bioregional Assessment Programme (XXXX) Belyando Basin Boundary - QLD Structural Framework. Bioregional Assessment Derived Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/4add856a-eb40-4bb2-bd41-f89788884782.

Dataset Ancestors

• Derived From Queensland geology and structural framework - GIS data July 2012

Site a water tower shared by two towns at the midpoint and determine the costs involved using the Pythagorean theorem. THE GEOINQUIRIES™ COLLECTION FOR MATHEMATICShttp://www.esri.com/geoinquiriesThe GeoInquiry™ collection for Mathematics contains 15 free, standards-based activities that correspond and extend spatial concepts found in course textbooks frequently used in introductory algebra or geometry classes. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the Common Core math national curriculum standards. Activities include:· Rates & Proportions: A lost beach· D=R x T· Linear rate of change: Steady growth· How much rain? Linear equations· Rates of population change· Distance and midpoint· The coordinate plane· Euclidean vs Non-Euclidean· Area and perimeter at the mall· Measuring crop circles· Area of complex figures· Similar triangles· Perpendicular bisectors· Centers of triangles· Volume of pyramids

Teachers, GeoMentors, and school administrators can learn more at http://www.esri.com/geoinquiries.

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the training samples were created and saved, the raster was then reclassified using the Image Classification Wizard tool in ArcGIS Pro, using the Support...

Explore Chris McCandless’ journey into the wilds of Alaska and the factors that led to his death. Book: Into the Wild by Jon Krakauer. THE GEOINQUIRIES™ COLLECTION FOR AMERICAN LITERATUREhttp://www.esri.com/geoinquiriesThe GeoInquiry™ collection for American Literature contains 15 free, standards-based activities that correspond and extend map-based concepts found in course texts frequently used in high school literature. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the Common Core ELA national curriculum standards. Activities include:· Beyond religion: Scarlet Letter · Virus of fear: Witchcraft in Salem· Poe and the Red Death· The Red Badge of Courage· Twain: Travel blogger· Hurricane warning· Gatsby: Then and now· Our town, your town· The mockingbird sings for freedom· Depression, dust and Steinbeck· Hiroshima· Dr. King's road to a Birmingham aail· Finding Mango Street· F451: Ban or burn the books· Surviving the wild

Teachers, GeoMentors, and school administrators can learn more at http://www.esri.com/geoinquiries.