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At least 55 records · Page 3

Noise analysis of spaceborne IR spatial interferometer

Analysis is performed to determine the relationship between the signal-to-noise ratio and the integration time for a spaceborne IR heterodyne spatial interferometer. The analysis includes consideration of the transformation of the interferogram to obtain the angular intensity distribution. The results presented show that for an IR source such as IRC + 10216, an integration time of 50 seconds per interferogram point will yield a signal-to-noise ratio greater than 10 dB in the angular intensity profile obtained from a 30-point transformation.

Wolfe, R. H., Jr.↗

Hierarchical analysis of spatial pattern and processes of Douglas-fir forests

There has been an increased interest in the quantification of pattern in ecological systems over the past years. This interest is motivated by the desire to construct valid models which extend across many scales. Spatial methods must quantify pattern, discriminate types of pattern, and relate hierarchical phenomena across scales. Wavelet analysis is introduced as a method to identify spatial structure in ecological transect data. The main advantage of the wavelet transform over other methods is its ability to preserve and display hierarchical information while allowing for pattern decomposition. Two applications of wavelet analysis are illustrated, as a means to: (1) quantify known spatial patterns in Douglas-fir forests at several scales, and (2) construct spatially-explicit hypotheses regarding pattern generating mechanisms. Application of the wavelet variance, derived from the wavelet transform, is developed for forest ecosystem analysis to obtain additional insight into spatially-explicit data. Specifically, the resolution capabilities of the wavelet variance are compared to the semi-variogram and Fourier power spectra for the description of spatial data using a set of one-dimensional stationary and non-stationary processes. The wavelet cross-covariance function is derived from the wavelet transform and introduced as a alternative method for the analysis of multivariate spatial data of understory vegetation and canopy in Douglas-fir forests of the western Cascades of Oregon.

Bradshaw, G. A.↗

A microprocessor-based one dimensional optical data processor for spatial frequency analysis

A high degree of accuracy was obtained in measuring the spatial frequency spectrum of known samples using an optical data processor based on a microprocessor, which reliably collected intensity versus angle data. Stray light control, system alignment, and angle measurement problems were addressed and solved. The capabilities of the instrument were extended by the addition of appropriate optics to allow the use of different wavelengths of laser radiation and by increasing the travel limits of the rotating arm to + or - 160 degrees. The acquisition, storage, and plotting of data by the computer permits the researcher a free hand in data manipulation such as subtracting background scattering from a diffraction pattern. Tests conducted to verify the operation of the processor using a 25 mm diameter pinhole, a 39.37 line pairs per mm series of multiple slits, and a microscope slide coated with 1.091 mm diameter polystyrene latex spheres are described.

Collier, R. L.↗

Procedures for analysis of spatial relationships among ship survey data and sea surface temperature

The establishment of a limited spatial data base for the U.S. eastern seaboard vicinity is discussed along with the demonstration of computer assisted analysis techniques for investigating spatial patterns and relationships among ship survey data and remotely sensed sea surface temperature. Ship survey variables included concentrations of two zooplankton, two icthyoplankton, and two fish species, in addition to physical data of depth to bottom and surface and bottom water temperatures. Continuous spatial distributions of these data were created by both weighted nearest neighbor and iterative smoothing interpolation techniques. Maps of surface water temperature were created by digitizing GOES satellite images. All mapped data were spatially registered by conversion of latitude and longitude coordinates to rotated Lambert conic conformal rectilinear coordinates and stored in grid format of approximately one hundred square kilometers per cell. The analysis of these data include the generation of statistical summaries and maps describing the joint occurrence among variables.

Berry, J. K.↗

The Land Use and Land Cover Dichotomy: A Comparison of Two Land Classification Systems in Support of Urban Earth Science Applications

One is likely to read the terms 'land use' and 'land cover' in the same sentence, yet these concepts have different origins and different applications. Land cover is typically analyzed by earth scientists working with remotely sensed images. Land use is typically studied by urban planners who must prescribe solutions that could prevent future problems. This apparent dichotomy has led to different classification systems for land-based data. The works of earth scientists and urban planning practitioners are beginning to come together in the field of spatial analysis and in their common use of new spatial analysis technology. In this context, the technology can stimulate a common 'language' that allows a broader sharing of ideas. The increasing amount of land use and land cover change challenges the various efforts to classify in ways that are efficient, effective, and agreeable to all groups of users. If land cover and land uses can be identified by remote methods using aerial photography and satellites, then these ways are more efficient than field surveys of the same area. New technology, such as high-resolution satellite sensors, and new methods, such as more refined algorithms for image interpretation, are providing refined data to better identify the actual cover and apparent use of land, thus effectiveness is improved. However, the closer together and the more vertical the land uses are, the more difficult the task of identification is, and the greater is the need to supplement remotely sensed data with field study (in situ). Thus, a number of land classification methods were developed in order to organize the greatly expanding volume of data on land characteristics in ways useful to different groups. This paper distinguishes two land based classification systems, one developed primarily for remotely sensed data, and the other, a more comprehensive system requiring in situ collection methods. The intent is to look at how the two systems developed and how they can work together so that land based information can be shared among different users and compared over time.

McAllister, William K.↗

Satellite-derived cyanobacteria frequency and magnitude in headwaters & near-dam reservoir surface waters of the Southern U.S.

Reservoirs are dominant features of the modern hydrologic landscape and provide vital services. However, the unique morphology of reservoirs can create suitable conditions for excessive algae growth and associated cyanobacteria blooms in shallow in-flow reservoir locations by providing warm water environments with relatively high nutrient inputs, deposition, and nutrient storage. Cyanobacteria harmful algal blooms (cyanoHAB) are costly water management issues and bloom recurrence is associated with economic costs and negative impacts to human, animal, and environmental health. As cyanoHAB occurrence varies substantially within different regions of a water body, understanding in-lake cyanoHAB spatial dynamics is essential to guide reservoir monitoring and mitigate potential public exposure to cyanotoxins. Cloud-based computational processing power and high temporal frequency of satellites enables advanced pixel-based spatial analysis of cyanoHAB frequency and quantitative assessment of reservoir headwater in-flows compared to near-dam surface waters of individual reservoirs. Additionally, extensive spatial coverage of satellite imagery allows for evaluation of spatial trends across many dozens of reservoir sites. Surface water cyanobacteria concentrations for sixty reservoirs in the southern U.S. were estimated using 300m resolution European Space Agency (ESA) Ocean and Land Colour Instrument (OLCI) satellite sensor for a five year period (May 2016–April 2021). Of the reservoirs studied, spatial analysis of OLCI data revealed 98% had more frequent cyanoHAB occurrence above the concentration of >100,000 cells/mL in headwaters compared to near-dam surface waters (P < 0.001). Headwaters exhibited greater seasonal variability with more frequent and higher magnitude cyanoHABs occurring mid-summer to fall. Examination of reservoirs identified extremely high concentration cyanobacteria events (>1,000,000 cells/mL) occurring in 70% of headwater locations while only 30% of near-dam locations exceeded this threshold. Wilcoxon signed-rank tests of cyanoHAB magnitudes using paired-observations (dates with observations in both a reservoir's headwater and near-dam locations) confirmed significantly higher concentrations in headwater versus near-dam locations (p < 0.001).

cyanobacteria↗

Land Cover Change Analysis and Spatial Variations in Southeast Asian Nations: Insights on Spatial Scale Dynamics

Land cover type is a fundamental aspect of studies using remote sensing for environmental analysis such as monitoring deforestation, quantifying wildland fire emissions, and more. There are many land cover products available for varying time periods and spatial resolutions, each with different land cover class definitions and number of land cover classes. These differences yield inherent variation in land cover estimates. In this study, we explore the spatial and areal variation between two major datasets for their commonly available time of 2018 to 2022 (MODIS 500m Land Cover and Esri Sentinel-2 10m land cover) in the continental Southeast Asia countries of Cambodia, Laos, Myanmar, Thailand, and Vietnam. To enable comparison, we resampled the datasets and reclassified the land cover classes to a common scheme. Major disagreement was detected between Esri and MODIS, especially for the wetlands/flooded vegetation class in which only 3.6% of pixels were in agreement. In addition to quantifying variation between the datasets, we quantify land cover change at the decadal scale of 2001, 2011, and 2021 for each country and identified province-level hotspots of land cover change. Cambodia experienced the highest rate of land cover change with 67.8% of total land area changed, followed by Vietnam (58.6%), Laos (50.2%), Myanmar (46.9%), and Thailand (46.3%). The highest rate of land cover change was detected in Odtar Mean Chey, Cambodia, which had large swaths of forest area cleared for agricultural production, river damming, and more. Ultimately, many areas in the region experienced forest clearing for crop production (i.e. rice) which appeared to be classified as wetland in the MODIS product, and crop in the Esri product. Myanmar was the only country which did not experience net decline in forest area over the 2001 to 2021 time period.

Land Cover Change↗

Spectral analysis of the intra-group medium in the NGC 2300 group

The results of spectral and spatial analysis of overlapping Rosat position sensitive proportional counter (PSPC) and Advanced Satellite for Cosmology and Astrophysics (ASCA) scanning imaging spectroradiometer (SIS) observations of the NGC 2300 group are presented. The spatial analysis of the co-added fields reveals that the diffuse X-ray gas can be traced to at least 25 arcmin. The temperature of the gas was found to be approximately 0.88 keV. The mass of gas within 0.33 Mpc is equal to 1.39 x 10(exp 12) solar mass. Comparing the mass of the galaxies plus the mass of hot gas the total mass of the system yields an observed baryonic fraction of 12 percent to 18 percent.

Davis, David S.↗

Large-scale spatially explicit analysis of carbon capture at cellulosic biorefineries

The large-scale production of cellulosic biofuels would involve spatially distributed systems including biomass fields, logistics networks and biorefineries. Better understanding of the interactions between landscape-related decisions and the design of biorefineries with carbon capture and storage (CCS) in a supply chain context is needed to enable efficient systems. Here we analyse the cost and greenhouse gas mitigation potential for cellulosic biofuel supply chains in the US Midwest using realistic spatially explicit land availability and crop productivity data and consider fuel conversion technologies with detailed CCS design for their associated CO 2 streams. Optimization methods identify trade-offs and design strategies leading to systems with attractive environmental and economic performance. Strategic and operational decisions depend on underlying spatial features and are sensitive to biofuel demand and CCS incentives. US CCS incentives neglect to motivate greenhouse gas mitigation from all supply chain emission sources, which leverage spatial interactions between CCS, electricity prices and the biomass landscape.

09 BIOMASS FUELS↗

Estimating noise levels of remotely sensed measurements from satellites using spatial structure analysis

A technique is presented whereby the noise level of satellite measurements of the atmosphere and earth can be estimated. The technique analyzes a spatial array of data measured by a satellite instrument. A minimum of about 200 satellite measurements is required, preferably in a regular pattern. Statistical structure analysis is used to describe a combination of the mean gradient and noise in the data. The noise level is then estimated by separating out the gradient information and leaving only the noise. Results are presented for four satellite sounding instruments, and effective blackbody or brightness temperature noise levels were compared to prelaunch specifications or inflight calibrations for each instrument. Comparisons showed that in the absence of cloud-contaminated measurements (in the case of infrared data) and away from the highly variable ground surface, the noise level of various satellite instruments can be obtained without the need for calibration data. The noise levels imply how much spatial averaging is possible, without smearing the detected geophysical gradient, and how much is necessary, to meet the absolute signal accuracy requirements for the intended use of the satellite measurements.

Hillger, Donald W.↗

Spatial frequency analysis of multispectral data.

This paper presents the definitions of texture dependent features which can be obtained in terms of the spatial frequencies of small sections of remotely sensed multispectral data. The features are made independent of the direction of view by defining them as symmetric functions of the spatial frequencies sensed with various viewing directions. Several textural features are defined and experimental results indicating existence of signatures in these features are presented. Preliminary experiments have been performed on the classification of 60 samples, 10 from each of the following 6 categories - grass, trees, water, staked tomatoes, treated ground tomatoes, and untreated ground tomatoes. Classifications of the training samples using only one feature at a time indicate that several of the features yield classification efficiencies higher than 65%. The efficiency increases considerably when combinations of these features are used.

Ramapriyan, H. K.↗

iGlobe Interactive Visualization and Analysis of Spatial Data

iGlobe is open-source software built on NASA World Wind virtual globe technology. iGlobe provides a growing set of tools for weather science, climate research, and agricultural analysis. Up until now, these types of sophisticated tools have been developed in isolation by national agencies, academic institutions, and research organizations. By providing an open-source solution to analyze and visualize weather, climate, and agricultural data, the scientific and research communities can more readily advance solutions needed to understand better the dynamics of our home planet, Earth

Hogan, Patrick↗

Satellite-derived Cyanobacteria Frequency and Magnitude in Headwaters & Near-dam Reservoir Surface Waters of the Southern U.S.

Reservoirs are dominant features of the modern hydrologic landscape and provide vital services. However, the unique morphology of reservoirs can create suitable conditions for excessive algae growth and associated cyanobacteria blooms in shallow in-flow reservoir locations by providing warm water environments with relatively high nutrient inputs, deposition, and nutrient storage. Cyanobacteria harmful algal blooms (cyanoHAB) are costly water management issues and bloom recurrence is associated with economic costs and negative impacts to human, animal, and environmental health. As cyanoHAB occurrence varies substantially within different regions of a water body, understanding in-lake cyanoHAB spatial dynamics is essential to guide reservoir monitoring and mitigate potential public exposure to cyanotoxins. Cloud-based computational processing power and high temporal frequency of satellites enables advanced pixel-based spatial analysis of cyanoHAB frequency and quantitative assessment of reservoir headwater in-flows compared to near-dam surface waters of individual reservoirs. Additionally, extensive spatial coverage of satellite imagery allows for evaluation of spatial trends across many dozens of reservoir sites. Surface water cyanobacteria concentrations for sixty reservoirs in the southern U.S. were estimated using 300 m resolution European Space Agency (ESA) Ocean and Land Colour Instrument (OLCI) satellite sensor for a five year period (May 2016–April 2021). Of the reservoirs studied, spatial analysis of OLCI data revealed 98% had more frequent cyanoHAB occurrence above the concentration of >100,000 cells/mL in headwaters compared to near-dam surface waters (P < 0.001). Headwaters exhibited greater seasonal variability with more frequent and higher magnitude cyanoHABs occurring mid-summer to fall. Examination of reservoirs identified extremely high concentration cyanobacteria events (>1,000,000 cells/mL) occurring in 70% of headwater locations while only 30% of near-dam locations exceeded this threshold. Wilcoxon signed-rank tests of cyanoHAB magnitudes using paired-observations (dates with observations in both a reservoir's headwater and near-dam locations) confirmed significantly higher concentrations in headwater versus near-dam locations (p < 0.001).

Amber R Ignatius↗

Quantifying and Zoning Urban Heat Island Effects Using Unsupervised Machine Learning

This work explores the Urban Heat Island (UHI) effects in Maricopa County, Arizona, employing a simulation-based approach that combines large-scale building energy modeling with advanced spatial analysis. Utilizing the Automatic Building Energy Modeling (AutoBEM) software suite, we simulated the energy consumption for approximately 1.35 million buildings based on the Model America version 1.0 (MAv1) dataset. Our methodology incorporated spatial analysis at multiple scales, including individual buildings, clusters of zones determined by K-means clustering, and geographical level evaluation based on Zip codes. The results revealed significant variations in energy consumption and heat emissions across different building types and urban zones. High-emission hotspots identified through clustering pointed to areas most contributing to the UHI effects. Zip code-based area analysis further contextualized these findings, offering an urban context-based perspective on emission distribution and informing potential urban energy policies for mitigating UHI effects.

Chowdhury, Shovan [ORNL]↗

HarDWR - Harmonized Water Rights Records

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Recalculated based on data sourced from WestDAAT - Changed using a Site ID column to identify unique records to using aa combination of Site ID and Allocation ID - Removed the Water Management Area (WMA) column from the harmonized records. The replacement is a separate file which stores the relationship between allocations and WMAs. This allows for allocations to contribute to water right amounts to multiple WMAs during the subsequent cumulative process. - Added a column describing a water rights legal status - Added "Unspecified" was a water source category - Added an acre-foot (AF) column - Added a column for the classification of the right's owner v1.02 - Added a .RData file to the dataset as a convenience for anyone exploring our code. This is an internal file, and the one referenced in analysis scripts as the data objects are already in R data objects. v1.01 - Updated the names of each file with an ID number less than 3 digits to include leading 0s v1.0 - Initial public release Description Here we present an updated database of Western U.S. water right records. This database provides consistent unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of seven broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of inter-sectoral water allocation changes though water markets (Grogan et al., *in review*). Specifically, the data were formatted for use as input to a process-based hydrologic model, Water Balance Model (WBM), with a water rights module (Grogan et al., *in review*). While this specific study motivated the development of the database presented here, water management in the U.S. West is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al., 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. West. We produced the water rights database presented here in four main steps: (1) data collection, (2) data quality control, (3) data harmonization, and (4) generation of cumulative water rights curves. Each of steps (1)-(3) had to be completed in order to produce (4), the final product that was used in the modeling exercise in Grogan et al. (*in review*). All data in each step is associated with a spatial unit called a Water Management Area (WMA), which is the unit of water right administration utilized by the state in which the right came from. Steps (2) and (3) required use to make assumptions and interpretation, and to remove records from the raw data collection. We describe each of these assumptions and interpretations below so that other researchers can choose to implement alternative assumptions an interpretation as fits their research aims. Motivation for Changing Data Sources The most significant change has been a switch from collecting the raw water rights directly from each state to using the water rights records presented in WestDAAT, a product of the Water Data Exchange (WaDE) Program under the Western States Water Council (WSWC). One of the main reasons for this is that each state of interest is a member of the WSWC, meaning that WaDE is partially funded by these states, as well as many universities. As WestDAAT is also a database with consistent categorization, it has allowed us to spend less time on data collection and quality control and more time on answering research questions. This has included records from water right sources we had previously not known about when creating v1.0 of this database. The only major downside to utilizing the WestDAAT records as our raw data is that further updates are tied to when WestDAAT is updated, as some states update their public water right records daily. However, as our focus is on cumulative water amounts at the regional scale, it is unlikely most records updates would have a significant effect on our results. The structure of WestDAAT led to several important changes to how HarWR is formatted. The most significant change is that WaDE has calculated a field known as `SiteUUID`, which is a unique identifier for the Point of Diversion (POD), or where the water is drawn from. This separate from `AllocationNativeID`, which is the identifier for the allocation of water, or the amount of water associated with the water right. It should be noted that it is possible for a single site to have multiple allocations associated with it and for an allocation to be able to be extracted from multiple sites. The site-allocation structure has allowed us to adapt a more consistent, and hopefully more realistic, approach in organizing the water right records than we had with HarDWR v1.0. This was incredibly helpful as the raw data from many states had multiple water uses within a single field within a single row of their raw data, and it was not always clear if the first water use was the most important, or simply first alphabetically. WestDAAT has already addressed this data quality issue. Furthermore, with v1.0, when there were multiple records with the same water right ID, we selected the largest volume or flow amount and disregarded the rest. As WestDAAT was already a common structure for disparate data formats, we were better able to identify sites with multiple allocations and, perhaps more importantly, allocations with multiple sites. This is particularly helpful when an allocation has sites which cross WMA boundaries, instead of just assigning the full water amount to a single WMA we are now able to divide the amount of water between the number of relevant WMAs. As it is now possible to identify allocations with water used in multiple WMAs, it is no longer practical to store this information within a single column. Instead the stAllocationToWMATab.csv file was created, which is an allocation by WMA matrix containing the percent Place of Use area overlap with each WMA. We then use this percentage to divide the allocation's flow amount between the given WMAs during the cumulation process to hopefully provide more realistic totals of water use in each area. However, not every state provides areas of water use, so like HarDWR v1.0, a hierarchical decision tree was used to assign each allocation to a WMA. First, if a WMA could be identified based on the allocation ID, then that WMA was used; typically, when available, this applied to the entire state and no further steps were needed. Second was the spatial analysis of Place of Use to WMAs. Third was a spatial analysis of the POD locations to WMAs, with the assumption that allocation's POD is within the WMA it should belong to; if an allocation still had multiple WMAs based on its POD locations, then the allocation's flow amount would be divided equally between all WMAs. The fourth, and final, process was to include water allocations which spatially fell outside of the state WMA boundaries. This could be due to several reasons, such as coordinate errors / imprecision in the POD location, imprecision in the WMA boundaries, or rights attached with features, such as a reservoir, which crosses state boundaries. To include these records, we decided for any POD which was within one kilometer of the state's edge would be assigned to the nearest WMA. Other Changes WestDAAT has Allowed In addition to a more nuanced and consistent method of assigning water right's data to WMAs, there are other benefits gained from using the WestDAAT dataset. Among those is a consistent categorization of a water right's legal status. In HarDWR v1.0, legal status was effectively ignored, which led to many valid concerns about the quality of the database related to the amounts of water the rights allowed to be claimed. The main issue was that rights with legal status' such as "application withdrawn", "non-active", or "cancelled" were included within HarDWR v1.0. These, and other water rights status' which were deemed to not be in use have been removed from this version of the database. Another major change has been the addition of the "unspecified water source category. This is water that can come from either surface water or groundwater, or the source of which is unknown. The addition of this source category brings the total number of categories to three. Due to reviewer feedback, we decided to add the acre-foot (AF) column so that the data may be more applicable to a wider audience. We added the ownerClassification column so that the data may be more applicable to a wider audience. File Descriptions The dataset is a series of various files organized by state sub-directories. In addition, each file begins with the state's name, in case the file is separate from its sub-directory for some reason. After the state name is the text which describes the contents of the file. Here is each file described in detail. Note that st is a placeholder for the state's name. stFullRecords_HarmonizedRights.csv: A file of the complete water records for each state. The column headers for each of this type of file are: state - The name of the state to which the allocations belong to. FIPS - The two digit numeric state ID code. siteID - The site location ID for POD locations. A site may have multiple allocations, which are the actual amount of water which can be drawn. In a simplified hypothetical, a farm stead may have an allocation for "irrigation" and an allocation for "domestic" water use, but the water is drawn from the same pumping equipment. It should be noted that many of the site ID appear to have been added by WaDE, and therefore may not be recognized by a given state's water rights database. allocationID - The allocation ID for the water right. For most states this is the water right ID, and what is recommended to use should a right be looked up on a given state's water rights database. The water amounts associated with these IDs tend to be finer scaled than those associated with siteID. It should be noted that some allocations may be extracted from multiple sites, particularly for larger Places of Use. ownerClassification - A classification of the types of owners for water rights. The most common is `Private` which incorporates a wide range of entities. Several classifications would be grouped into a government category, most of which are for the U.S. Federal Government. These allocations could be listed as "Federal", "United States of America", or as the names of any number of federal agencies. The last major grouping of entities is for "Native American"s. priorityDate - The date we use as the water right priority date for our modeling analysis. This is the legal priority date when it is available. However, for some rights, specifically from California and New Mexico, we used a pseudo priority date (e.g. well completion date or start of well drilling date) when a legal priority date was not available. The most questionable dates come from New Mexico, where the only date associated with certain water right records was the date the allocation was recorded in the database. As the allocation record creation tended to be within a few months of the filing of the application of the water right, from manually double checking the water rights, and our analysis focuses on aggregating water rights on the timescale of years, we determined it was acceptable to use such dates to include as many records as possible. primaryBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories WestDAAT. This column is the original WaDE category for the primary water use at the PoD site. allocationBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories for WestDAAT. This column is the original WaDE category

Economics↗

A Spatial-Temporal Analysis of Travel Time Gap and Inequality between Public Transportation and Personal Vehicles

The increased use of personal vehicles presents environmental challenges, prompting the exploration of public transportation as an affordable, eco-friendly alternative. However, obstacles like fixed schedules, limited routes, and extended travel times impede widespread adoption. This study investigates the temporal evolution of spatial inequality in the travel time gap between public transportation and personal vehicles, reflecting disparities across states and time periods. Analyzing Census Transportation Planning Program data for six northeastern states in 2010 and 2016 reveals no significant increase in the travel time gap, but notable growth in inequality in a few urban and disadvantaged communities. Comprehending these trends is vital for fostering equitable advancements in transportation infrastructure and enhancing public transportation competitiveness.

Pan, Melrose↗

GIScience in the era of Artificial Intelligence: a research agenda towards Autonomous GIS

The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic knowledge production, driving geographic information systems (GIS) towards autonomous GIS. Leveraging LLMs as the decision core, autonomous GIS can independently generate and execute geoprocessing workflows to perform spatial analysis. In this vision paper, we further elaborate on the concept of autonomous GIS and present a conceptual framework that defines its five autonomous goals, five levels of autonomy, five core functions, and three operational scales. We demonstrate how autonomous GIS could perform geospatial data retrieval, spatial analysis, and map making with four proof-of-concept GIS agents. We conclude by identifying critical challenges and future research directions, including fine-tuning and self-growing decision-cores, autonomous modelling, and examining the societal and practical implications of autonomous GIS. By establishing the groundwork for a paradigm shift in GIScience, this paper envisions a future where GIS moves beyond traditional workflows to autonomously reason, derive, innovate, and advance geospatial solutions to pressing global challenges. Meanwhile, we emphasize that as we design and deploy increasingly intelligent geospatial systems, we carry a responsibility to ensure they are developed in socially responsible ways, serve the public good, and support the continued value of human geographic insight in an AI-augmented future.

Autonomous GI↗