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.

Through the Department of the Interior-Bureau of Indian Affairs Enterprise License Agreement (DOI-BIA ELA) program, BIA employees and employees of federally-recognized Tribes may access a variety of geographic information systems (GIS) online courses and instructor-led training events throughout the year at no cost to them. These online GIS courses and instructor-led training events are hosted by the Branch of Geospatial Support (BOGS) or offered by BOGS in partnership with other organizations and federal agencies. Online courses are self-paced and available year-round, while instructor-led training events have limited capacity and require registration and attendance on specific dates. This dataset does not any training where the course was not completed by the participant or where training was cancelled or otherwise not able to be completed. Point locations depict BIA Office locations or Tribal Office Headquarters. For completed trainings where a participant _location was not provided a point locations may not be available. For more information on the Branch of Geospatial Support Geospatial training program, please visit:https://www.bia.gov/service/geospatial-training.

This dataset holds all materials for the Inform E-learning GIS course

Seattle Parks and Recreation Golf Course locations. SPR Golf Courses are managed by contractors.Refresh Cycle: WeeklyFeature Class: DPR.GolfCourse

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

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.

This data was collected by the U.S. Bureau of Land Management (BLM) in New Mexico at both the New Mexico State Office and at the various field offices. This dataset is meant to depict the surface owner or manager of the land parcels. In the vast majority of land parcels, they will be one and the same. However, there are instances where the owner and manager of the land surface are not the same. When this occurs, the manager of the land is usually indicated. BLM's Master Title Plats are the official land records of the federal government and serve as the primary data source for depiction of all federal lands. Information from State of New Mexico is the primary source for the depiction of all state lands. Auxilliary source are referenced, as well, for the depiction of all lands. Collection of this dataset began in the 1980's using the BLM's ADS software to digitize information at the 1:24,000 scale. In the mid to late 1990's the data was converted from ADS to ArcInfo software and merged into tiles of one degree of longitude by one half degree of latitude. These tiles were regularly updated. The tiles were merged into a statewide coverage. The source geodatabase for this shapefile was created by loading the merged ArcInfo coverage into a personal geodatabase. The geodatabase data were snapped to a more accurate GCDB derived land network, where available. In areas where GCDB was not available the data were snapped to digitized PLSS. In 2006, the personal geodatabase was loaded into an enterprise geodatabase (SDE). This shapefile has been created by exporting the feature class from SDE.

The goal of the Lead Safe Certificate program is to prevent lead poisoning by ensuring that all rental homes built prior to 1978 are compliant with the city's Lead Safe Ordinance and maintained free of lead hazards. Any home built before 1978 is reasonably presumed to contain lead-based paint. Residential rental units built before 1978 must have a Lead Safe Certification from the City of Cleveland’s Department of Building and Housing. The Lead Safe Certification is only valid for two years, after which rental property owners must re-apply for certification. For more information about the City's Lead Safe Certification program, please visit this Building & Housing page. RelatedLead Safe Certificate Explorer Data GlossarySee the Attributes section below for details about each column in this dataset.ContactCity of Cleveland, Building and Housing Lead Compliance Program Update FrequencyWeekly on Sundays at 7 AM EST (6 AM during daylight savings)

The Minnesota Department of Agriculture’s “Minnesota Agricultural Water Quality Certification Program” (MAWQCP) launched an assessment tool online application to provide program participants a common online format in which to streamline the certification process.
The intended application audience includes the producer, licensed certifier, crop advisor, or other agronomic and conservation professionals. The assessment tool, one of three steps necessary to obtain certification, is a risk assessment tool which aggregates factors relating to nutrient management, tillage, soil properties, pest management and conservation practices into a unitless index score on a 1 thru 10 scores. Each field and cropping scenario is assessed.
The online application increases the efficiency of information gathering necessary to run the assessment tool. Features include mapping queries, data organization into field libraries, summary report generation and data packaging into small, easily transmittable formats. The application uses GIS map and geoprocessing services to calculate some of the summary data.
The online application ensures producer privacy by requiring users to store the information on a local drive; the hosting website and server does not store any information.

A public feature layer view used to share natural spaces set aside for recreation or the protection of wildlife or natural habitats.

This dataset is OBSOLETE as of 11/18/2024 and will be removed from ArcGIS Online on 11/18/2025.An updated version of this dataset is available at Certified Sustainable Buildings | Open Data Portal | City of Cambridge.A map of the updated data can be found in two places:Certified Sustainable Buildings Map | Open Data Portal | City of CambridgeSustainable Buildings Map - City of Cambridge, MAThis point layer shows the location of sustainable buildings in Cambridge. For inclusion in this layer, a building must do at least one of the following: qualify for the City’s Article 22 regulatory process; be certified by Passive House; be certified by Enterprise Green Communities; or be certified by LEEDunder a LEED version that requires the whole building to meet sustainability standards. Some buildings meet two or more of these criteria. Additionally, this layer contains information about other certifications (Energy Star, Fitwel, and WELL) that may apply to the included buildings. If an included building participates in the City’s BEUDO regulatory process, this layer provides two key emissions figures for the building. Information provided about the applicable sustainable building programs for qualifying buildings includes certification levels, certification types, ratings, or scores. This layer includes data from City and non-City sources.Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription BldgID type: Stringwidth: 50precision: 0 Unique ID for database from GIS.

Latitude type: Doublewidth: 8precision: 38 Geographic coordinate from GIS Bldg ID centroid file.

Longitude type: Doublewidth: 8precision: 38 Geographic coordinate from GIS Bldg ID centroid file.

Article22_SystemLevelEquivalenc type: Stringwidth: 150precision: 0

Article22 type: Stringwidth: 3precision: 0 "Yes" indicates Article 22 building.

BEUDO_TotalGHGEmissionsIntensit type: Doublewidth: 8precision: 38

BEUDO type: Stringwidth: 3precision: 0 "Yes" indicates BUEDO building.

BEUDO_SourceEUI type: Doublewidth: 8precision: 38 A critical variable for reporting about BEUDO.

EnergyStar type: Stringwidth: 3precision: 0 "Yes" indicates EnergyStar building.

EnergyStar_CountYearsCert type: SmallIntegerwidth: 2precision: 5 Number of years certified. EnergyStar certification may be renewed annually.

EnergyStar_LastYearCert type: Stringwidth: 4precision: 0 Year of last certification.

EnergyStar_LastCertScore type: SmallIntegerwidth: 2precision: 5 Most recent EnergyStar score.

EnterpriseGC type: Stringwidth: 3precision: 0 "Yes" indicates Enterprise Green Communities building.

EnterpriseGC_CertTemplate type: Stringwidth: 100precision: 0 Certification version.

EnterpriseGC_PointsAchieved type: SmallIntegerwidth: 2precision: 5 Enterprise Green Communities score.

Fitwel type: Stringwidth: 3precision: 0 "Yes" indicates Fitwel building.

Fitwel_StarRating type: SmallIntegerwidth: 2precision: 5 Numerical Fitwel rating.

LEED type: Stringwidth: 3precision: 0 "Yes" indicates LEED building.

LEED_TotalCerts type: SmallIntegerwidth: 2precision: 5 Number of certifications applying to the whole building. The LEED fields contain details about certifications that are "whole-building," not referring to one part of the building only or or to building operations.

LEED_LastCertDate type: Datewidth: 8precision: 0 Date of last certification applying to the whole building.

LEED_LastSystemVersion type: Stringwidth: 100precision: 0 Certification version and rating system.

LEED_LastCertLevel type: Stringwidth: 50precision: 0 LEED certifictation level at which whole building is certified. Certified/Silver/Gold/Platinum: Does not not include "registered" buildings.

PassiveHouse type: Stringwidth: 3precision: 0 "Yes" indicates Passive House building.

PassiveHouse_CertVersion type: Stringwidth: 100precision: 0 Certification version.

WELL type: Stringwidth: 3precision: 0 "Yes" indicates WELL building.

WELL_Version type: Stringwidth: 50precision: 0 Certification version.

WELL_ProjectType type: Stringwidth: 150precision: 0 WELL project type.

WELL_CertLevel type: Stringwidth: 50precision: 0 Certification level. Certified Pilot/Compliance/Bronze/Silver/Gold/Platinum or Health-Safety Rated: Does not include "registered" or "precertified" buildings.

created_date type: Datewidth: 8precision: 0

last_edited_date type: Datewidth: 8precision: 0

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

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

This layer shows the location of designated centres under Financial Assistance Scheme for designated evening adult education courses in Hong Kong. It is a set of the data made available by the Education Bureau under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong CSDI Portal at https://portal.csdi.gov.hk.