A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community-level patterns and ecological processes. In this study, we develop a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub-blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of Stipa grandis in grazed and ungrazed S. grandis communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of S. grandis by using the pair correlation function g(r) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of S. grandis were overdispersed according to the homogeneous Poisson process at 0-0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a Geographical Information System, to collect experimental data on the spatial point patterns for the populations in this grassland community.

Methods 1. Data collection using digital photographs and GIS

A flat 5 m x 5 m sampling block was chosen in a study grassland community and divided with bamboo chopsticks into 100 sub-blocks of 50 cm x 50 cm (Fig. 1). A digital camera was then mounted to a telescoping stake and positioned in the center of each sub-block to photograph vegetation within a 0.25 m2 area. Pictures were taken 1.75 m above the ground at an approximate downward angle of 90° (Fig. 2). Automatic camera settings were used for focus, lighting and shutter speed. After photographing the plot as a whole, photographs were taken of each individual plant in each sub-block. In order to identify each individual plant from the digital images, each plant was uniquely marked before the pictures were taken (Fig. 2 B).

Digital images were imported into a computer as JPEG files, and the position of each plant in the pictures was determined using GIS. This involved four steps: 1) A reference frame (Fig. 3) was established using R2V software to designate control points, or the four vertexes of each sub-block (Appendix S1), so that all plants in each sub-block were within the same reference frame. The parallax and optical distortion in the raster images was then geometrically corrected based on these selected control points; 2) Maps, or layers in GIS terminology, were set up for each species as PROJECT files (Appendix S2), and all individuals in each sub-block were digitized using R2V software (Appendix S3). For accuracy, the digitization of plant individual locations was performed manually; 3) Each plant species layer was exported from a PROJECT file to a SHAPE file in R2V software (Appendix S4); 4) Finally each species layer was opened in Arc GIS software in the SHAPE file format, and attribute data from each species layer was exported into Arc GIS to obtain the precise coordinates for each species. This last phase involved four steps of its own, from adding the data (Appendix S5), to opening the attribute table (Appendix S6), to adding new x and y coordinate fields (Appendix S7) and to obtaining the x and y coordinates and filling in the new fields (Appendix S8).

1. Data reliability assessment

To determine the accuracy of our new method, we measured the individual locations of Leymus chinensis, a perennial rhizome grass, in representative community blocks 5 m x 5 m in size in typical steppe habitat in the Inner Mongolia Autonomous Region of China in July 2010 (Fig. 4 A). As our standard for comparison, we used a ruler to measure the individual coordinates of L. chinensis. We tested for significant differences between (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler (see section 3.2 Data Analysis). If (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler, did not differ significantly, then we could conclude that our new method of measuring the coordinates of L. chinensis was reliable.

We compared the results using a t-test (Table 1). We found no significant differences in either (1) the coordinates of L. chinensis or (2) the pair correlation function g of L. chinensis. Further, we compared the pattern characteristics of L. chinensis when measured by our new method against the ruler measurements using a null model. We found that the two pattern characteristics of L. chinensis did not differ significantly based on the homogenous Poisson process or complete spatial randomness (Fig. 4 B). Thus, we concluded that the data obtained using our new method was reliable enough to perform point pattern analysis with a null model in grassland communities.

The global GIS Data Collector market is experiencing robust growth, driven by increasing adoption of precision agriculture techniques, expanding infrastructure development projects, and the rising need for accurate geospatial data across various industries. The market's Compound Annual Growth Rate (CAGR) is estimated to be around 8% for the forecast period of 2025-2033, projecting significant market expansion. This growth is fueled by technological advancements in GPS technology, improved data processing capabilities, and the increasing affordability of GIS data collection devices. Key segments driving market expansion include high-precision data collection systems and their application in agriculture, where farmers are increasingly leveraging real-time data for optimized resource management and increased yields. The industrial sector also contributes significantly to market growth, with applications ranging from construction and surveying to utility management and environmental monitoring. While the market faces certain restraints, such as the need for skilled professionals to operate the sophisticated equipment and the potential for data security concerns, these are outweighed by the overwhelming benefits of improved efficiency, accuracy, and cost savings provided by GIS data collectors. The market's regional landscape shows significant participation from North America and Europe, owing to established technological infrastructure and early adoption of advanced GIS technologies. However, rapid growth is expected in the Asia-Pacific region, especially in countries like China and India, fueled by infrastructure development and expanding agricultural activities. Leading players like Garmin, Trimble, and Hexagon are driving innovation and competition, while a growing number of regional players offer more cost-effective solutions. The competitive landscape is characterized by a mix of established global players and regional manufacturers. The established players leverage their technological expertise and extensive distribution networks to maintain market leadership. However, the increasing affordability and accessibility of GIS data collection technologies are attracting new entrants, creating a more dynamic market. Future growth will likely be shaped by the integration of artificial intelligence and machine learning into GIS data collection systems, further enhancing data processing capabilities and automation. The continued development of robust and user-friendly software applications will also contribute to market expansion. Furthermore, the adoption of cloud-based GIS platforms is expected to increase, facilitating greater data sharing and collaboration. This convergence of hardware and software innovations will drive market growth and broaden the applications of GIS data collectors across diverse sectors.

GIS Data Collector Market Outlook

According to our latest research, the global GIS Data Collector market size reached USD 6.8 billion in 2024, reflecting robust demand across multiple industries. The market is projected to grow at a healthy CAGR of 11.2% from 2025 to 2033, reaching an anticipated value of USD 19.7 billion by 2033. This significant expansion is driven by increasing adoption of geospatial technologies in urban planning, environmental monitoring, and the digital transformation strategies of enterprises worldwide. As per our findings, the surge in smart city initiatives and the proliferation of IoT-based mapping solutions are key contributors to the accelerating growth of the GIS Data Collector market globally.

The primary growth driver for the GIS Data Collector market is the escalating need for precise and real-time geospatial data across diverse sectors. Urbanization and the rapid expansion of metropolitan regions have intensified the demand for advanced mapping and surveying tools, enabling city planners and government agencies to make informed decisions. The integration of GIS data collectors with cutting-edge technologies such as artificial intelligence, machine learning, and cloud computing has further enhanced data accuracy and accessibility. As organizations seek to optimize resource allocation and improve operational efficiency, the utilization of GIS data collectors has become indispensable in applications ranging from infrastructure management to disaster response and land administration.

Another crucial factor propelling the market is the growing use of GIS data collectors in environmental monitoring and natural resource management. With the increasing frequency of climate-related events and the global emphasis on sustainability, accurate geospatial data is vital for tracking environmental changes, managing agricultural lands, and monitoring deforestation or water resources. Advanced GIS data collectors equipped with remote sensing and mobile mapping capabilities are enabling stakeholders to gather high-resolution data, analyze spatial patterns, and implement effective conservation strategies. The synergy between GIS and remote sensing technologies is empowering organizations to address environmental challenges more proactively and efficiently.

Technological advancements in data collection methods have also played a pivotal role in shaping the GIS Data Collector market landscape. The advent of unmanned aerial vehicles (UAVs), mobile mapping systems, and real-time kinematic (RTK) GPS has revolutionized the way geospatial data is captured and processed. These innovations have not only improved the accuracy and speed of data collection but have also reduced operational costs and enhanced safety in field surveys. The integration of GIS data collectors with cloud-based platforms allows seamless data sharing and collaboration, fostering a more connected and agile ecosystem for geospatial data management. As industries continue to digitize their operations, the demand for sophisticated and user-friendly GIS data collection solutions is expected to witness sustained growth.

Field Data Collection Software has become an integral component in the realm of GIS data collection, providing users with the capability to efficiently gather, process, and analyze geospatial data in real time. This software facilitates seamless integration with various data collection devices, such as GPS receivers and mobile mapping systems, enabling field operatives to capture high-precision data with ease. The adoption of Field Data Collection Software is particularly beneficial in sectors like urban planning and environmental monitoring, where timely and accurate data is crucial for decision-making. By leveraging cloud-based platforms, this software ensures that data collected in the field is instantly accessible to stakeholders, promoting collaboration and enhancing the overall efficiency of geospatial projects. As the demand for real-time data insights grows, the role of Field Data Collection Software in supporting dynamic and responsive GIS operations continues to expand.

From a regional perspective, North America currently dominates the GIS Data Collector market, followed closely by Europe and Asia Pacific. The strong presence of leading technology providers, substantial investments in smart infrastructure, and suppo

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Geographic Information System Analytics Market Size 2024-2028

The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.

The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.
Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.

What will be the Size of the GIS Analytics Market during the forecast period?

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The geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.
GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.

How is this Geographic Information System Analytics Industry segmented?

The geographic information system analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Retail and Real Estate
  Government
  Utilities
  Telecom
  Manufacturing and Automotive
  Agriculture
  Construction
  Mining
  Transportation
  Healthcare
  Defense and Intelligence
  Energy
  Education and Research
  BFSI


Components

  Software
  Services


Deployment Modes

  On-Premises
  Cloud-Based


Applications

  Urban and Regional Planning
  Disaster Management
  Environmental Monitoring Asset Management
  Surveying and Mapping
  Location-Based Services
  Geospatial Business Intelligence
  Natural Resource Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    South Korea


  Middle East and Africa

    UAE


  South America

    Brazil


  Rest of World

By End-user Insights

The retail and real estate segment is estimated to witness significant growth during the forecast period.

The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.

The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector, gover

As GIS and computing technologies advanced rapidly, many indoor space studies began to adopt GIS technology, data models, and analysis methods. However, even with a considerable amount of research on indoor GIS and various indoor systems developed for different applications, there has not been much attention devoted to adopting indoor GIS for the evaluation space usage. Applying indoor GIS for space usage assessment can not only provide a map-based interface for data collection, but also brings spatial analysis and reporting capabilities for this purpose. This study aims to explore best practice of using an indoor GIS platform to assess space usage and design a complete indoor GIS solution to facilitate and streamline the data collection, a management and reporting workflow. The design has a user-friendly interface for data collectors and an automated mechanism to aggregate and visualize the space usage statistics. A case study was carried out at the Purdue University Libraries to assess study space usage. The system is efficient and effective in collecting student counts and activities and generating reports to interested parties in a timely manner. The analysis results of the collected data provide insights into the user preferences in terms of space usage. This study demonstrates the advantages of applying an indoor GIS solution to evaluate space usage as well as providing a framework to design and implement such a system. The system can be easily extended and applied to other buildings for space usage assessment purposes with minimal development efforts.

This study focuses on the use of citizen science and GIS tools for collecting and analyzing data on Rose Swanson Mountain in British Columbia, Canada. While several organizations collect data on wildlife habitats, trail mapping, and fire documentation on the mountain, there are few studies conducted on the area and citizen science is not being addressed. The study aims to aggregate various data sources and involve citizens in the data collection process using ArcGIS Dashboard and ArcGIS Survey 123. These GIS tools allow for the integration and analysis of different kinds of data, as well as the creation of interactive maps and surveys that can facilitate citizen engagement and data collection. The data used in the dashboard was sourced from BC Data Catalogue, Explore the Map, and iNaturalist. Results show effective citizen participation, with 1073 wildlife observations and 3043 plant observations. The dashboard provides a user-friendly interface for citizens to tailor their map extent and layers, access surveys, and obtain information on each attribute included in the pop-up by clicking. Analysis on classification of fuel types, ecological communities, endangered wildlife species presence and critical habitat, and scope of human activities can be conducted based on the distribution of data. The dashboard can provide direction for researchers to develop research or contribute to other projects in progress, as well as advocate for natural resource managers to use citizen science data. The study demonstrates the potential for GIS and citizen science to contribute to meaningful discoveries and advancements in areas.

Geographic Information System (GIS) Market Outlook

The global Geographic Information System (GIS) market size was valued at approximately USD 8.1 billion in 2023 and is projected to reach around USD 16.3 billion by 2032, growing at a CAGR of 8.2% during the forecast period. One of the key growth factors driving this market is the increasing adoption of GIS technology across various industries such as agriculture, construction, and transportation, which is enhancing operational efficiencies and enabling better decision-making capabilities.

Several factors are contributing to the robust growth of the GIS market. Firstly, the increasing need for spatial data in urban planning, infrastructure development, and natural resource management is accelerating the demand for GIS solutions. For instance, governments and municipalities globally are increasingly relying on GIS for planning and managing urban sprawl, transportation systems, and utility networks. This growing reliance on spatial data for efficient resource allocation and policy-making is significantly propelling the GIS market.

Secondly, the advent of advanced technologies like the Internet of Things (IoT), Artificial Intelligence (AI), and machine learning is enhancing the capabilities of GIS systems. The integration of these technologies with GIS allows for real-time data analysis and predictive analytics, making GIS solutions more powerful and valuable. For example, AI-powered GIS can predict traffic patterns and help in effective city planning, while IoT-enabled GIS can monitor and manage utilities like water and electricity in real time, thus driving market growth.

Lastly, the rising focus on disaster management and environmental monitoring is further boosting the GIS market. Natural disasters like floods, hurricanes, and earthquakes necessitate the need for accurate and real-time spatial data to facilitate timely response and mitigation efforts. GIS technology plays a crucial role in disaster risk assessment, emergency response, and recovery planning, thereby increasing its adoption in disaster management agencies. Moreover, environmental monitoring for issues like deforestation, pollution, and climate change is becoming increasingly vital, and GIS is instrumental in tracking and addressing these challenges.

Regionally, the North American market is expected to hold a significant share due to the widespread adoption of advanced technologies and substantial investments in infrastructure development. Asia Pacific is anticipated to witness the fastest growth, driven by rapid urbanization, industrialization, and supportive government initiatives for smart city projects. Additionally, Europe is expected to show steady growth due to stringent regulations on environmental management and urban planning.

Component Analysis

The GIS market by component is segmented into hardware, software, and services. The hardware segment includes devices like GPS, imaging sensors, and other data capture devices. These tools are critical for collecting accurate spatial data, which forms the backbone of GIS solutions. The demand for advanced hardware components is rising, as organizations seek high-precision instruments for data collection. The advent of technologies such as LiDAR and drones has further enhanced the capabilities of GIS hardware, making data collection faster and more accurate.

In the software segment, GIS platforms and applications are used to store, analyze, and visualize spatial data. GIS software has seen significant advancements, with features like 3D mapping, real-time data integration, and cloud-based collaboration becoming increasingly prevalent. Companies are investing heavily in upgrading their GIS software to leverage these advanced features, thereby driving the growth of the software segment. Open-source GIS software is also gaining traction, providing cost-effective solutions for small and medium enterprises.

The services segment encompasses various professional services such as consulting, integration, maintenance, and training. As GIS solutions become more complex and sophisticated, the need for specialized services to implement and manage these systems is growing. Consulting services assist organizations in selecting the right GIS solutions and integrating them with existing systems. Maintenance and support services ensure that GIS systems operate efficiently and remain up-to-date with the latest technological advancements. Training services are also crucial, as they help users maximize the potential of GIS technologies.

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

This collection contains Environmental Response Management Application (ERMA) GIS layers used as part of the Programmatic Damage Assessment and Restoration Plan (PDARP), including outputs from Synthetic Aperture Radar (SAR) imagery, helicopter flights surveys (observations) of marine mammal and turtles, Mississippi Canyon 252 wellhead location, wellhead buffers, and supporting bathymetric contour data, infrared and photographic images from EPA's airborne spectral photometric environmental collection technology (ASPECT) with geospatial, chemical and radiological information, boom-related response observations, nearshore tissue and sediment samples, forensic and Total Polycyclic Aromatic Hydrocarbon (TPAH) results, stranded oil forensic classification data, and other types of chemistry data, Submerged Aquatic Vegetation (SAV) classifications, seabed sampling and transect data, sample locations for workplan cruises, deep-sea area injury toxicity results and total polycyclic aromatic hydrocarbon (TPAH) results, habitat injury zones, footprint impacts on mesophotic reef resources and other types of benthic habitat data, overflight imagery of the flight path for the NOAA King Air flights taken in October of 2010 and contains post-oiling images collection in support of Natural Resource Damage Assessment (NRDA) marsh monitoring, turtle survey overflight observations, loggerhead sea turtle density grids, sea turtle capture observations and transect analysis, sea turtle strandings, as well as probabilities of oiling and other related datasets, trawl locations, Southeast Area Monitoring and Assessment Program (SEAMAP) plankton trawls, workplan cruise samples, and other related data, delineation of the areas impacted with additional fresh water due to the opening of the diversions in 2011 as part of the Deepwater Horizon oil spill response, surface shoreline oiling characteristics as observed by field surveys performed by Shoreline Cleanup Assessment Techniques (SCAT) teams, marine mammal surveys, observations, telemetry and abundance data including Cytochrome P450 (CYP) dolphin analysis, population and abundance datasets, telemetry, wildlife and aerial observations, bathymetry estimates, and other related Marine Mammal field observations and surveys, presence and spatial distribution of synthetic-based mud (SBM) in deep-sea sediments around the Macondo well, surface sediment, residual kriging, and other oiling analytical data, oyster recruitment and abundance sampling results, estimates of subtidal habitat, estimates of oyster resource, seafloor substrate mapping layers, percent cover, nearshore and subtidal quadrat abundance data, and other related datasets, shoreline exposure model for beach and marsh oiling, wave exposure, habitat classifications, wetland monitoring datasets, and related shoreline datasets, compilation of all the individual Texture Classifying Neural Network Algorithm (TCNNA) days from Synthetic Aperture Radar (SAR) satellite polygons, a variety of cumulative oiling datasets including the Texture Classifying Neural Network Algorithm (TCNNA) from Synthetic Aperture Radar (SAR) satellite polygon layers, burn locations, dispersant operation datasets including estimations of where aerial dispersants were applied via aerial flight paths, dispersant airport locations, daily flight tracks, and vessel dispersant tracks, as well as locations of subsurface dispersant data, marine mammal surveys, observations, telemetry and abundance data collected including synoptic surveys, helicopter surveys, Cytochrome P450 (CYP) dolphin analysis, population and abundance datasets, telemetry, wildlife and aerial observations, bathymetry estimates, other related marine mammal field observations and surveys, and sea turtle data, and other data related to the Deepwater Horizon oil spill in the Northern Gulf of Mexico. Some of these data were collected during the response to the Mississippi Canyon 252 Deepwater Horizon oil spill in the Northern Gulf of Mexico.

Overview: This document is a reference guide for users of the SAR Field Data Collection Form User Guide. The purpose is to provide a better understanding of how to use the form in the field.

The underlying technology used with this form is likely to evolve and change over time, therefore technical user guides will be provided as appendices to this document.

Background: The SAR Field Data Collection Form was created by an interdisciplinary group of first responders, decision-makers and technology specialists from across Federal, State, and Local Urban Search and Rescue Teams – the NAPSG Foundation SAR Working Group. If you have any questions or concerns regarding this document and associated materials, please send a note to comments@publicsafetygis.org.

Purpose: The SAR Field Data Collection Form is intended to provide a standardized approach to the collection of information during disaster response alongside resource management and tracking of assets.The primary goal of this approach is to obtain situational awareness (where, when, what) for SAR Teams in the field across four relevant themes: Victims that may need assistance or have already been helped. Hazards that must be avoided or mitigated. Damage that have been rapidly assessed for damage, when time and the mission permits. Other mission critical intelligence that vary based on mission type. The secondary goal of this approach is to provide essential elements of information to those not currently on-scene of the disaster. Using the themes above, information and maps can be shared based on “need to know”. If you are a technology specialist looking to deploy this application on your own see the Deployment Kit.

GapMaps GIS Data by Azira uses location data on mobile phones sourced by Azira which is collected from smartphone apps when the users have given their permission to track their location. It can shed light on consumer visitation patterns (“where from” and “where to”), frequency of visits, profiles of consumers and much more.

Businesses can utilise GIS data to answer key questions including: - What is the demographic profile of customers visiting my locations? - What is my primary catchment? And where within that catchment do most of my customers travel from to reach my locations? - What points of interest drive customers to my locations (ie. work, shopping, recreation, hotel or education facilities that are in the area) ? - How far do customers travel to visit my locations? - Where are the potential gaps in my store network for new developments?
- What is the sales impact on an existing store if a new store is opened nearby? - Is my marketing strategy targeted to the right audience? - Where are my competitor's customers coming from?

Mobile Location data provides a range of benefits that make it a valuable GIS Data source for location intelligence services including: - Real-time - Low-cost at high scale - Accurate - Flexible - Non-proprietary - Empirical

Azira have created robust screening methods to evaluate the quality of Mobile location data collected from multiple sources to ensure that their data lake contains only the highest-quality mobile location data.

This includes partnering with trusted location SDK providers that get proper end user consent to track their location when they download an application, can detect device movement/visits and use GPS to determine location co-ordinates.

Data received from partners is put through Azira's data quality algorithm discarding data points that receive a low quality score.

Use cases in Europe will be considered on a case to case basis.

This U.S. Geological Survey (USGS) data release provides a digital geospatial database for the geologic map of the Bayhorse area, central Custer County, Idaho (Hobbs and others, 1991). Attribute tables and geospatial features (points, lines, and polygons) conform to the Geologic Map Schema (GeMS, 2020) and represent the geologic map as published in the USGS Miscellaneous Investigations Series Map I-1882 (Hobbs and others, 1991). The 357,167-acre map area represents the geology at a publication scale of 1:62,000. References: Hobbs, S.W., Hays, W.H., and McIntyre, D.H., 1991, Geologic map of the Bayhorse area, central Custer County, Idaho: U.S. Geological Survey, Miscellaneous Investigations Series Map I-1882, scale 1:62,500, https://doi.org/10.3133/i1882. U.S. Geological Survey National Cooperative Geologic Mapping Program, 2020, GeMS (Geologic Map Schema) - A standard format for the digital publication of geologic maps: U.S. Geological Survey Techniques and Methods, book 11, chap. B10, 74 p., https://doi.org//10.3133/tm11B10.

ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)

**When using the GIS data included in these map packages, please cite all of the following:

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. http://doi.org/10.1371/journal.pone.0220457

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. https://doi.org/10.5281/zenodo.2572018

OVERVIEW OF CONTENTS

This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:

Raw DEM and Soils data

Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the National Elevation Dataset)

DEM_Individual_Tiles: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.

DEMs_Merged: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM.

Soils Data (Map services and data available from Natural Resources Conservation Service Web Soil Survey, U.S. Department of Agriculture)

Animas-Dolores_Area_Soils: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).

Cortez_Area_Soils: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).

ArcGIS Map Packages

Goodman_Watershed_Full_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).

Goodman_Watershed_Mesa-Only_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.

Crow_Canyon_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).

Coffey_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).

For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."

LICENSES

Code: MIT year: 2019 Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton

CONTACT

Andrew Gillreath-Brown, PhD Candidate, RPA Department of Anthropology, Washington State University andrew.brown1234@gmail.com – Email andrewgillreathbrown.wordpress.com – Web

The Air, Water, and Aquatic Environments (AWAE) research program is one of eight Science Program areas within the Rocky Mountain Research Station (RMRS). Our science develops core knowledge, methods, and technologies that enable effective watershed management in forests and grasslands, sustain biodiversity, and maintain healthy watershed conditions. We conduct basic and applied research on the effects of natural processes and human activities on watershed resources, including interactions between aquatic and terrestrial ecosystems. The knowledge we develop supports management, conservation, and restoration of terrestrial, riparian and aquatic ecosystems and provides for sustainable clean air and water quality in the Interior West. With capabilities in atmospheric sciences, soils, forest engineering, biogeochemistry, hydrology, plant physiology, aquatic ecology and limnology, conservation biology and fisheries, our scientists focus on two key research problems: Core watershed research quantifies the dynamics of hydrologic, geomorphic and biogeochemical processes in forests and rangelands at multiple scales and defines the biological processes and patterns that affect the distribution, resilience, and persistence of native aquatic, riparian and terrestrial species. Integrated, interdisciplinary research explores the effects of climate variability and climate change on forest, grassland and aquatic ecosystems. Resources in this dataset:Resource Title: Projects, Tools, and Data. File Name: Web Page, url: https://www.fs.fed.us/rm/boise/AWAE/projects.html Projects include Air Temperature Monitoring and Modeling, Biogeochemistry Lab in Colorado, Rangewide Bull Trout eDNA Project, Climate Shield Cold-Water Refuge Streams for Native Trout, Cutthroat trout-rainbow trout hybridization - data downloads and maps, Fire and Aquatic Ecosystems science, Fish and Cattle Grazing reports, Geomophic Road Analysis and Inventory Package (GRAIP) tool for erosion and sediment delivery to streams, GRAIP_Lite - Geomophic Road Analysis and Inventory Package (GRAIP) tool for erosion and sediment delivery to streams, IF3: Integrating Forests, Fish, and Fire, National forest climate change maps: Your guide to the future, National forest contributions to streamflow, The National Stream Internet network, people, data, GIS, analysis, techniques, NorWeST Stream Temperature Regional Database and Model, River Bathymetry Toolkit (RBT), Sediment Transport Data for Idaho, Nevada, Wyoming, Colorado, SnowEx, Stream Temperature Modeling and Monitoring, Spatial Statistical Modeling on Stream netowrks - tools and GIS downloads, Understanding Sculpin DNA - environmental DNA and morphological species differences, Understanding the diversity of Cottusin western North America, Valley Bottom Confinement GIS tools, Water Erosion Prediction Project (WEPP), Great Lakes WEPP Watershed Online GIS Interface, Western Division AFS - 2008 Bull Trout Symposium - Bull Trout and Climate Change, Western US Stream Flow Metric Dataset

This U.S. Geological Survey (USGS) data release updates the digital geospatial database for the southern portion of the geologic map of the western part of the Cut Bank 1 degree x 2 degrees quadrangle, northwestern Montana (Harrison and others, 1998). Attribute tables and geospatial features (points, lines, and polygons) conform to the Geologic Map Schema (USGS NCGMP, 2020). The 899,246-acre map area represents the geology at a publication scale of 1:250,000. Minor errors, such as mistakes in line decoration or differences between the digital data and the map image, are corrected, and missing orientation points are included in this version. The map covers primarily Flathead, Glacier, Pondera, and Teton Counties, but also includes minor parts of Lake County. References: Harrison, J.E., Whipple, J.W., and Lidke, 1998, Geologic Map of the Western Part of the Cut Bank 1 degree x 2 degrees quadrangle, Northwestern Montana: U.S. Geological Survey Miscellaneous Investigations Series Map I-2593, version 1.0, 31 p., scale 1:250,000, https://ngmdb.usgs.gov/Prodesc/proddesc_13063.htm. U.S. Geological Survey National Cooperative Geologic Mapping Program, 2020, GeMS (Geologic Map Schema) - A standard format for the digital publication of geologic maps: U.S. Geological Survey Techniques and Methods, book 11, chap. B10, 74 p., https://doi.org//10.3133/tm11B10.

GIS Controller Market Outlook

The GIS Controller market size was valued at $8.3 billion in 2023 and is projected to reach $15.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.2% during the forecast period. This significant growth factor can be attributed primarily to increasing urbanization, the rising need for efficient spatial data management, and technological advancements in geospatial analytics.

One of the prime growth factors driving the GIS Controller market is the escalating demand for smart city solutions. As urbanization continues to rise globally, governments and municipalities are increasingly investing in smart city initiatives to improve urban planning, public safety, and resource management. GIS controllers play a crucial role in these initiatives by providing accurate spatial data, which is essential for efficient infrastructure development, traffic management, and environmental monitoring. Furthermore, the integration of GIS with other technologies such as IoT and AI is opening new avenues for real-time data analysis and decision-making, further propelling market growth.

The agriculture sector is another significant contributor to the growth of the GIS Controller market. Precision farming techniques that leverage GIS technology are gaining traction for their ability to enhance crop yield and optimize resource usage. By providing detailed insights into soil conditions, weather patterns, and crop health, GIS controllers enable farmers to make data-driven decisions, thereby improving operational efficiency and reducing costs. Additionally, government initiatives aimed at promoting sustainable farming practices are further fueling the adoption of GIS technology in the agricultural sector.

Disaster management is another critical application area where GIS controllers are making a substantial impact. The increasing frequency of natural disasters such as hurricanes, floods, and earthquakes necessitates advanced planning and real-time response capabilities. GIS controllers help in mapping disaster-prone areas, predicting the impact of natural calamities, and coordinating emergency response efforts. This capability is invaluable for minimizing damage and saving lives. The growing focus on disaster preparedness and management is expected to drive the demand for GIS controllers in the coming years.

Regionally, North America holds a significant share of the GIS Controller market, driven by the high adoption rate of advanced technologies and substantial investments in smart city projects. The Asia Pacific region is expected to witness the highest growth rate, fueled by rapid urbanization, infrastructural development, and increasing government initiatives for digital transformation. Europe also presents substantial growth opportunities due to the rising focus on environmental sustainability and smart transportation systems.

Component Analysis

The GIS Controller market is segmented into three primary components: Hardware, Software, and Services. The hardware segment includes devices and equipment necessary for capturing and processing geospatial data, such as GPS units, sensors, and data collection devices. This segment is witnessing steady growth due to the increasing need for advanced and accurate data collection tools. The integration of AI and IoT with GIS hardware is further enhancing the capabilities of these devices, making them indispensable for various applications such as urban planning, agriculture, and disaster management.

In terms of software, GIS Controllers are equipped with specialized software for data analysis, mapping, and modeling. This segment is experiencing rapid growth due to the increasing demand for sophisticated analytical tools that can handle large datasets and provide real-time insights. Advanced GIS software solutions are being developed to offer more user-friendly interfaces and better integration with other enterprise systems, thereby enhancing their usability and effectiveness across different sectors. The rise of cloud-based GIS software is also contributing to the growth of this segment by offering scalable and cost-effective solutions.

The services segment comprises consultancy, implementation, and maintenance services essential for the effective deployment and utilization of GIS Controllers. As organizations increasingly adopt GIS technology, the demand for specialized services that can ensure smooth integration and optimal performance is rising. Professional services providers are offering customized solutions to meet the specific needs of different industries

The Cook County Bureau of Technology (BOT) and the U.S. Geological Survey (USGS) have collaborated on a comprehensive project to survey and map selected surface water bodies in Cook County, Illinois. This initiative addresses critical needs in surface-water and storm water management, emergency response, construction permitting, and aquatic habitat research. The carefully selected water bodies were surveyed based on size, accessibility, existing data availability, and current usage. The project involved collecting, processing, and analyzing bathymetric data and ancillary water-quality information. Notably, the collected data has been meticulously preprocessed into high-resolution lake polygons and detailed 2-foot interval contours. By combining cutting-edge data collection techniques with advanced preprocessing methods, this project sets a new standard for aquatic mapping in urban areas, ultimately contributing to more sustainable and effective water management practices in Cook County.

Metadata record for data from AAS (ASAC) project 3130.

Public High latitude terrestrial ecosystems are experiencing rapid change, which is most likely caused by climate change, human impacts, and invasive species. Up-to-date and accurate spatial data at a range of scales are of crucial importance for mapping changes in these fragile ecosystems. The aim of this study is to undertake spatial analyses on the changing terrestrial ecosystems of the Windmill Islands, Antarctica and sub-Antarctic Macquarie Island. The study aims to better understand the different processes that result in ecosystem change and with new state-of-the-art high-resolution spatial data we hope to contribute to improved management strategies.

Project Objectives: Introduction

Environmental threats globally can be categorised into four main types: local impact from human activity and habitat loss; impact from alien species and homogenisation of biota; impact from climate change and impact associated with harvesting and resource extraction. All four types of impacts occur to some degree in the Antarctic region (Hull and Bergstrom 2006, Bergstrom and Selkirk 2007). This project examines change associated with these impacts in Australian Antarctic and sub-Antarctic territories. In particular, we seek to isolate signals of impact from regional climate change from those of other human-induced change within Antarctic and sub-Antarctic terrestrial ecosystems.

This project will develop and apply spatial data collection and analysis techniques for detailed baseline mapping and change detection of vegetation communities on the Windmill Islands and Macquarie Island. We will then employ these cutting-edge techniques to quantify, detect, and understand the impact of changes. In detail, the objectives of this project are to:

Objective 1: Collate and collect spatial data in order to establish a baseline map of, and detect changes in vegetation communities on the Windmill Islands and Macquarie Island.

Objective 2: Create high-resolution digital elevation models (DEM) based on GPS data and airborne laser scanning (LiDAR) of the localities.

Objective 3: Explore ecological relationships between vegetation communities and biologically relevant landscape characteristics and human-induced disturbance using terrain analysis of digital elevation models in a Geographical Information System (GIS) in order to better understand the distribution of and changes in vegetation communities. This will include the development of hydrological terrain analyses to examine the impact of changing snow conditions around Casey on vegetation communities.

Objective 4: Develop and apply new multi-scale field sampling techniques based on field photogrammetry and GPS observations at different scales (from 20cm to 20m) to measure relative percent cover of plant species and vegetation communities. This objective is of key importance to bridge the range of scale levels from small field quadrats to satellite images that cover large portions of the landscape.

Objective 5: Combine detailed plot-scale data and field photographs with terrain information and high-resolution satellite imagery to identify and map changes in both plant communities and plant stress more efficiently.

This project will deliver valuable baseline and temporal data on the impact of environmental change in Australian Antarctic and sub-Antarctic territories. It will improve our understanding of Antarctic and sub-Antarctic landscape ecology and species adaptations. It will provide a predictive GIS model that can forecast the effects of human activities in Antarctica and provide new tools for spatial multi-scale geographic analysis.

Taken from the 2009-2010 Progress Report: Progress against objectives: Objective 1: Windmill Islands moss beds In the first year of this project we found that the spatial scale of the moss beds (tens of m2) makes satellite imagery (even very high resolution imagery of 0.5 m) unsuitable for mapping their extent in sufficient detail. Due to logistical constraints aerial photography is impractical. Recent developments in the use of unmanned aerial vehicles (UAVs) for remote sensing applications provide exciting new opportunities for ultra-high resolution mapping and monitoring of the environment. This year, we developed a new UAV consisting of an electric remote controlled helicopter capable of carrying three different cameras: visible colour, near-infrared, and thermal infrared for cost-effective, efficient, and ultra-high resolution (less than 5 cm pixel size) mapping of terrestrial vegetation in the Windmill Islands, Antarctica. These three sensors allowed us to map different physical characteristics of the moss beds at resolutions of several centimetres. We had a very successful season at Casey. We managed to collect spatial data for four different moss sites: ASPA135, Red Shed, Robinson Ridge, ASPA136. We collected the following datasets: - Very accurate GPS locations for existing moss quadrat sites with a geodetic GPS receiver (cm accuracy). - For ASPA135, Red Shed, and Robinson Ridge we collected very dense GPS transects and used these data to interpolate high resolution digital elevation models (DEMs). - For all sites we collected geotagged photographs of all quadrats in addition to geotagged landscape scale photographs. - For ASPA135, Red Shed, and Robinson Ridge we flew a total of 26 UAV flights collecting visible photography (2 cm pixel size), near-infrared photography, thermal imagery, and video footage for all sites. - For the Robinson Ridge and Red Shed site we collected spectral signatures of the key moss species and other land cover types (water, rock types, lichen, snow, etc.). The handheld spectrometer was rented from Geoscience Australia. - On request of Sandra Potter and Tom Maggs, we collected GPS data and UAV photography for the Casey quarry before and after blasting to determine the extent of the blasting zone and to acquire ultra-high resolution imagery of the quarry for management purposes.

Macquarie Island This project has strong links with AAS project 3095. Phillippa Bricher (UTAS PhD student) and Jared Abdul-Rahman (UTAS volunteer and Honours student) have collected data for Phillippa's PhD project. Data collection for Phillippa's project consisted of geotagged photographs of vegetation plots with Polecam. Jared concentrated on photographing Azorella die-back. Phillippa's data will be used for vegetation classification of the island using satellite imagery and DEMs. A new WorldView-2 high-resolution satellite image was acquired for the northern half of the island on 26 December 2009. This image will be extremely useful for vegetation classification and change detection.

Objective 2 As noted in objective 1 (above), we collected dense transects of GPS data for three moss bed sites in the Windmill Islands. We interpolated the GPS height values to obtain three very high resolution DEMs (less than 0.5 m). The AAD's LiDAR instrument was not available at Casey or Macquarie Island this season, however, we requested LiDAR data collection at Davis over known moss sites. The data was collected successfully, but it hasn't been processed yet. With this dataset we are hoping to assess the usefulness of LiDAR for mapping of micro-topography. In the meantime we have continued to develop our UAV (externally funded UTAS project). We have built a larger version that is capable of carrying a mini-LiDAR instrument. We hope to employ this UAV LiDAR at our study sites in the Windmill Islands during the 2010/2011 summer season. This novel system will allow us to capture the microtopography of the moss bed areas and will allows us to more accurately model the hydrological conditions (compared to GPS derived DEMs).

Objective 3: We have already modelled several environmental parameters for the high-resolution DEMs of the Windmill Islands (ASPA135, Robinson Ridge, and the Red Shed). The derivatives include a topographic wetness index, average annual solar radiation, and slope gradient. In combination with the UAV photographs and the close-up quadrat photographs we aim to establish a relationship between the condition of the moss and environmental factors. Lucieer is currently on Study Leave at ITC in The Netherlands (March - April 2010) and the University of Calgary, Canada (April - May 2010). At these institutes Lucieer is working on a new texture-based classification technique to map healthy tussock slopes on Macquarie Island (as an indicator of island health). Preliminary highlight that this novel image classification technique is very successful at identifying tussock slopes in high resolution QuickBird imagery.

Objective 4: With the Polecam technique on Macquarie Island and with the UAV photographs in the Windmill Islands we have developed two very novel techniques for multi-scale sampling. These photographic sampling techniques will provide invaluable information for the next phase of the project.

Objective 5: We aim to further develop our UAV project and use the larger UAV with multiple sensor in future field campaigns. This will allow us to build a multi-temporal dataset of the study areas and detect changes over time. The experiments in this first field season have provided us with important insights for suitable data collection techniques and the collected data are incredibly valuable for addressing the objectives of this project.

Abstract

The Population and Housing Census 2000 was prepared and conducted according to the recommendations of the United Nations Economic Commission for Europe and the Statistical Office of the European Communities (Eurostat), which guarantee that the census data are internationally comparable. Also the comparability with the data of previous censuses carried out in Estonia was taken into account. Census 2000 was carried out from March 31 to April 9.

The Statistical Office of Estonia was responsible for conducting the Census. The purpose of the Census was to collect data on the size, composition and distribution of the country's population and access housing stock and conditions. The moment of the Census was 00.00 on 31 March 2000; the data collected in the Census reflect the characteristics of housing and of the population as of the moment of the Census.

The content of the Census data and the data collection methods were developed in the Statistical Office in cooperation with the experts of different fields. Regulation of the Government of the Republic 5 March 1999 approved the Census questionnaires and Census rules.

Geographic coverage

The Census covered all country.

The Statistical Office of Estonia (SOE) launched the mapping programme for the 2000 Population and Housing Census in 1995. After completing the test areas the specifications for the digital Census maps were finalized. According to the Specification, 1:50 000 maps in rural areas and 1:5 000 maps in urban areas were drawn. The specification was optimized to create a cartographic basis for the Census planning (Census area (CA) delineation) and for the Census itself (maps for enumerators, maps for supervisors, etc.). The Census mapping process was outsourced from SOE. The work was done by two companies - one in urban, another in rural areas. The production methodology was different in urban and rural areas. In rural areas, paper maps of the 1989 Census were used as a base source material, digitized by the mapping company and updated by local governments. In urban areas, the existing maps and orthophotos were used as a base source and the maps were updated by the mapping company. For rural and urban areas the municipalities compiled household lists including the number of inhabitants in each building or apartment. The purpose of household lists was to provide information about the number of inhabitants for the delineation of enumeration areas (EA).

The borders of Census units were marked on digital Population Census maps and the maps were printed for Census purposes. SOE stores digital maps in urban areas in Mapinfo, in rural areas in ArcView software and household lists in Foxpro software. The Census maps were ready by December 1999. Digital Population Census maps with the registered borders of administrative and settlement units are the basis for presenting the Census results in a cartographic way and for the development of Census GIS.

Universe

The Census covered: - persons who were in the Republic of Estonia at the moment of the Census (March 31, at 00.00) (excluding the diplomatic staff of foreign diplomatic missions and consular posts and their family members and persons in active service in foreign army); - persons who resided in the Republic of Estonia but who were in foreign states temporarily for a term of up to one year; - diplomatic staff of diplomatic missions and consular posts of the Republic of Estonia and their family members, who were in a foreign state at the moment of the Census; - residential buildings and other buildings used for habitation, and apartments and other dwellings situated therein (excluding buildings of foreign diplomatic missions and consular posts and dwellings situated therein).

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

PHC 2000 was conducted using two types of questionnaires - the Personal Questionnaire containing 31 questions, and the Housing Questionnaire with 12 questions. The Census questionnaires collected personal, household information as well as dwelling data.

1. Personal data include: 1.1. first and surname; personal identification code; 1.2. person’s and his/her parents’ place of birth, person’s permanent place of residence and location at the Census moment, person’s permanent place of residence on 12 January 1989, year of arrival in Estonia, address of the place of work; 1.3. sex, date of birth, citizenship, ethnic nationality, mother tongue, knowledge of languages (answering the question is voluntary), marital status, number of children given birth to, mother’s age at the time of birth of the first child; 1.4. main sources of subsistence, length of working week in the week preceding the Census (number of hours worked), social status (in military service, not working, actively seeking work, ready to start work, student (pupil), pensioner, homemaker, not working for other reasons), name of the main place of work / main employer (answering the question is voluntary), economic activity of the main place of work, employment status at the main place of work (employee with stable contract, other employee, entrepreneur-employer, farmer with salaried employees, self-employed person, freelancer, farmer without salaried employees, contributing family workers in a family enterprise, farm, member of commercial association), occupation at main place of work, length of usual working week; 1.5. level of curriculum that the person has completed or studies currently, highest level of vocational or professional education completed, highest level of general education completed; 1.6. long-term disability or illness determined by the medical commission of experts; 1.7. religious affiliation and faith confessed (answering the question is voluntary).

2. Household data describe: 2.1. type of institution; 2.2. list of household members, relationship of each household member to the reference person, family relationships between the household members, permanent and temporary members of the household, duration of absence of a permanent household member in months, duration of presence of a temporary household member; 2.3. legal basis for the use of the dwelling; 2.4. the links between the household and agricultural activity.

3. Data on dwelling include: 3.1. type, form of ownership, total area, number of rooms, existence of a kitchen, plumbing and heating (water supply system, sewage disposal system, hot water, bath (shower), sauna, flush toilet, electricity, gas, central heating, electric heating); 3.2. address, type and period of construction of the building containing dwellings.

Cleaning operations

Two scanners were used for optical data entry. The application software for data processing were worked out in co-operation with the company AS AboBase Systems and based on Oracle tools. The scanning of the Census questionnaires was performed in 2000 from 10 May to 22 September. During that period 3,505,451 questionnaires were scanned. 135 operators who had passed the training were engaged in the data processing.

Data appraisal

For evaluating the coverage of the Census and the quality of the Census data, a post-enumeration sample survey was organized. It covered about 1% of the population and a stratified random sample of enumeration areas was drawn. The post-enumeration survey was carried out from 14 to 19 April 2000 in 50 enumeration areas. Comparison of the Census data and the data collected in the post-enumeration survey showed that the undercoverage of the Census was on an average 1.2%.

The following data is provided as a public service, for informational purposes only. This data should not be construed as legal advice. Users of this data should independently verify its determinations prior to taking any action under the California Environmental Quality Act (CEQA) or any other law. The State of California makes no warranties as to accuracy of this data.This zoning data was collected from 535 of California"s 539 jurisdictions. An effort was made to contact each jurisdiction in the state and request zoning data in whatever form available. In the event that zoning maps were not available in a GIS format, maps were converted from PDF or image maps using geo-referencing techniques and then transposing map information to parcel geometries sourced from county assessor data. Collection efforts began in late 2021 and were mostly finished in late 2022. Some data has been updated in 2023. Sources and dates are documented in the "Source" and "Date" columns with more detail available in the accompanying sources table.Individual zoning maps were combined for this statewide dataset. As part of the aggregation process, contiguous areas with identical zone codes, within jurisdictions, were merged or dissolved. Some features representing roads with right-of-way or Null zone designations were removed from this data. Features less than 4 square meters in area were also removed.