Spatial Analysis Of Geospatial Data: Processing And Use Cases (2022)

The first attempts of spatial data analysis date back to the 1960s and belong to Canada. The earliest objective for GIS applications was the systematization of the country’s natural resources. Spatial analysis in GIS has expanded worldwide ever since. It is difficult to imagine a single entrepreneur or a superindustry that does not benefit from georeferencing in this or that way.

Spatial analysis allows determining if the selected area is suitable for particular business activities, detecting changes, identifying tendencies, assessing risks, outlining possible outcomes, and preventing losses.

Up-to-date tools and spatial analysis techniques provide more accurate analytics than decades ago.

What Is Spatial Analysis?

Geospatial analysis is a process of GIS data interpretation, exploration and modeling, from acquisition to understanding results. The retrieved information is computer-processed with spatial analysis software and varies depending on the number of tasks and their complexity. The simplest one is visualization, while a more detailed approach suggests comprehensive analytics with specific tools to elaborate actionable insights.

Examples of spatial analysis include measuring distances and shapes, setting routes and tracking transportations, establishing correlations between objects, events, and places via referring their locations to geographical positions (both live and historical).

Typically, spatial analysis consists of five key stages: understanding your goal, preparing data, choosing suitable tools and techniques, performing the research, and estimating results.

So, first of all, it is important to figure out what you are interested to know. The next will be to choose corresponding spatial analysis methods for data manipulation and interpretation. Knowing exactly what you want to get, you will be able to tailor the research techniques to your needs. Once you determine, you start the next stage of spatial analysis – data processing and interpretation. Last, you estimate the results to define if you achieved your goal or not.

Spatial Analysis In GIS: Set Up Your Objectives

The peculiar feature of GIS spatial analysis and modeling is its flexibility. You can match as many layers as you like and get different results accordingly.

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The scope of applications covers numerous branches including agriculture, forestry, marine sciences, oil and gas, mining, demography, economy, to mention a few. In particular, spatial analysis for agriculture defines vegetation density, degree of soil moisture and soil temperature, crop state, etc. It enables farmers to allocate resources in the most efficient way (like sparing distribution of chemicals). In forestry, spatial data analytics helps to spot deforestation and predict fires by critical temperatures.

Relying on spatial analysis results, oceanologists identify oil spills, and demographists can decide if educational and healthcare institutions are sufficient to meet the population needs of the studied region.

With spatial data analysis in GIS, logistic specialists build up the fastest or safest routes, retailers see the most advantaged store location, and rescue teams allocate aid to the most affected areas.

For complex questions in spatial analysis, it is important to split them into more specific ones. Answering each of them, you will reach a goal step by step.

For example, a chain store owner may be interested in: “What is the best location for a new shop in LA?” This query can comprise several minor ones:

  • How many competitors provide the same services in LA?
  • What districts of LA the target customers live in?
  • How big is this population in the questioned areas?
  • Are the competitors’ shops distributed evenly?
  • Are there any areas with faraway stores that provide the same services?
  • What is the mean income across LA districts where the potential customers live?

Spatial Data Analysis: What You Need For A Proper Start

To carry out successful spatial analytics research, it is important to prepare everything in advance. So, make sure you have all the required data for examination and refine the content to the state you need. Pre-processing in spatial analysis may generally require several preliminary steps like geometric and spectral correction, radiometric correction of atmospheric effects, missing pixel restoration, contrasting, and filtration.

Geometric Correction

Geometric rectification for spatial data analysis suggests geographic reference of images and improvement of geometric discrepancies due to several reasons that match differently for different satellite imagery types.

(Video) What is Spatial Data Science?

The Earth’s surface is not even. Due to its spherical shape, the most accurate captures are those made at nadir (the central scan line). Correspondingly, a farther position from nadir involves greater distortions.

The relief curves impact image precision as well. However, the great distance between the satellite’s orbit and Earth makes the effect smoother, so in spatial analyses, it is usually ignored. The exceptions are high-elevation areas, e.g., mountainous regions.

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Another significant aspect in spatial analysis is that our planet is constantly moving around its orbit and around the Sun. It shifts its position by 0.25 every single minute, and this rotation affects the focus. Besides, the satellite is orbiting Earth in its turn, and revolving the image quality as well.

Spectral Correction

Satellite imagery initially has the so-called “raw” brightness values (digital numbers). This data format does not allow a proper comparison of images retrieved from various sources. For this reason, spectral (or radiometric) correction in spatial analysis is applied to adjust digital numbers to physically meaningful units, i.e. actual surface reflectance or emittance values.

Radiometric Atmospheric Correction

Image quality also depends on atmospheric conditions that weaken the signal both from the sensor and from the target. Scattering due to atmospheric aerosols (dust, mist, fog, carbon dioxide, methane, etc) alongside radiation absorption and cloud cover are the major factors affecting the pixel brightness and thus requiring additional adjustment.

There exist various methods to reduce atmospheric effects in spatial analysis, e.g., image-based calculations or modeling. Mathematical techniques imply modeling different atmospheric conditions depending on the season, weather conditions, aerosols, etc.

Besides, the optical properties of water bodies are similar to the ones of absolutely black objects in the red and infrared bands. Knowing it, a spatial analysis expert can easily distinguish clouds and haze against the marine background.

Missing Pixels Restoration

Some information necessary for spatial analysis may be missing due to system errors during data acquisition or transmission, contrails, etc. The most common reconstruction method of dropped lines for spatial data analysis suggests taking pixels from neighboring lines or making the average of the two. Even the replacement implies certain errors, it facilitates further image interpretation.

Image Contrast Enhancement

The contrast in photography is the difference in minimum and maximum luminance, or brightness and color saturation, which allows distinguishing the object and its contours against the background. Low contrast is a typical issue to fix. In particular, contours for spatial analysis and modeling can be better outlined with visual imagery decoding.

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Digital image processing for contrast enhancement in spatial analysis involves the following techniques of histogram modifications:

  • Linear expansion generates new values to the input image pixels extending the value range.
  • Normalization in spatial data analysis in GIS expands the most intensive histogram area.
  • Equalization corrects pixel brightness to make their number equal or similar at each brightness level.

Image Filtering

Filtering in spatial data analysis outlines required objects and reduces noises by means of sliding window modifications with recalculations of existing pixel values and assigning new ones. The new values are established mathematical functions from neighboring pixels. Formula coefficients depend on decoding tasks. The window may comprise 3×3 or 5×5 pixels and moves by one pixel until the whole image is covered.

Apart from sharpening and noise removal, the filtering technique is applied for smoothing and contour edging.

Equip Yourself With Suitable Spatial Analysis Techniques

A significant advantage of digital imagery is the possibility of its computer processing, which can be carried out not only to prepare data but provide geospatial analysis proper. Ideally, the whole analytical process is automated and completed by a machine. Yet, it rarely happens practically. In most cases, specific tools facilitate a scientist’s research.

Color Transformations

A single channel of multispectral imagery is displayed in gray. It becomes colorful only in the combination of three channels, including red, green, and blue (the so-called RGB model). In spatial data analysis, the image can be presented in natural or false colors, depending on whether RGB channels coincide or are replaced by other bands.

(Video) What is Spatial Data - An Introduction to Spatial Data and its Applications

Prevailing tones are determined by channel brightness. For example, high pixel values for the R channel will give mostly reddish hues. Correspondingly, dominating brightness of the G channel will result in shades of green.

While it is easier to identify the objects in natural colors, false-color images in geospatial data analysis allow distinguishing between them and outlining their contours.

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Indexed Images

The method of indexed images employs recalculating brightness values for each pixel in different channels with established mathematical operations. This way, pixels are represented for analysis not in their absolute brightness but are assigned new indexed values according to the colormap matrix. Indexation highlights the target objects and simplifies the research.

Principal Components Analysis

The technique of principal components analysis correlates multispectral data, which implies that higher brightness in one band involves higher brightness in the other bands as well.

This method of geospatial data analysis allows the following manipulations:

  • creating an output image with three principal components among three and more bands ignoring minor components, and thus eliminating noises;
  • enhancing vaguely distinguished objects on single-component spatial imagery;
  • comparing spatial image series captured within different time frameworks to understand the dynamics and changes;
  • reducing the spatial data size with minimum information lost.

Spectral Unmixing

The given method of pre-processing in spatial analysis applies to define targets much smaller than the pixel size on images with multiple objects. In this case, an analyst compares the retrieved data with existing pure spectrum registries understanding quantitative discrepancies between a pure spectrum and mixes for each pixel spectrum. The obtained image will allow distinguishing the main element by its coloring.

Classifications

Classification in spatial analysis is computer imagery decoding or automated pixel categorizing into specific classes to differentiate the objects (hence the technique name derives). Classification can be performed with and without training data.

Classification With Training Data

This classification type in geospatial analysis relies on comparing pixel brightness with certain samples. Analyzing correspondences, the operator can refer particular objects to the most suitable classes (e.g., in urban imagery there can be buildings, roads, vegetation). This method works if the scenery components are known, they are less than thirty in number and easily distinguished on the spatial image.

The most widely-used spatial analysis techniques of this classification type include:

  • method of spectral angle in spatial analytics applies for the classification of objects with similar brightness values;
  • parallelepiped classification is used in spatial analytics when brightness scales do not intersect;
  • minimum distance estimation in spatial analysis works when brightness scales intersect;
  • Mahalanobis distance provides more accurate measurements in multivariate space, compared to the previous technique;
  • maximum likelihood estimation aligns the object to a group based on maximum probability calculations. The method is employed in spatial data analysis when brightness scales overlap and have a complicated shape;
  • binary splitting divides all spatial image pixels into two groups.

Classification Without Training Data

These algorithms automatically separate pixels via statistical brightness distribution and are applicable when objects are not identified or exceed thirty in number. Classifications without training data can be used in spatial analysis prior to ones with training.

Examples of the most typical unsupervised categorizations for spatial data analysis include ISODATA and K-Means clustering:

  • ISODATA is the acronym for the Iterative Self-Organizing Data Analysis Technique. It relies on referring pixels with the closest brightness values in the spectral feature space.
  • K-Means clustering in spatial analysis is similar to ISODATA but requires certain basic mean values to categorize the objects.

Start Up The Analysis Process

Performing the spatial analysis proper is the next stage after shaping the problem, pre-processing preparations, and choosing the most suitable techniques. Once the preliminary steps are completed, a spatial data analyst focuses on obtaining qualitative and quantitative results.

Consider The Output Information

Data retrieval should be followed by detailed research if the objectives of spatial analytics were achieved or not and how the results can give answers to similar questions in the area of study and other industries.

This stage of spatial data analysis also suggests consideration of practical use and relevance of acquired information by consulting credible sources. For example, when elaborating a crop map of the U.S. with satellite data, the results should correspond to the official governmental statistics.

Another issue is to figure out if the information obtained with spatial analysis can be validated, i.e. how accurate it is. The last but not the least point is to assess the data quality by its quantity, deciding if the results are missing, sufficient, excessive, etc.

(Video) Practical Geospatial Analysis of Open and Public-Use Data

A good point of spatial analysis is visualization for better understanding like static and interactive charts, graphs, diagrams, tables, or maps.

How is Spatial Analysis Performed In EOSDA Products?

Geospatial data analysis has plenty of applications in various industries, and agriculture, forestry, and ecology in particular. It allows monitoring the current state and boosting profit on the one hand, and raising environmental awareness and addressing natural calamities on the other. Furthermore, spatial imagery enables experts to inspect hard-to-reach areas without being physically present there, model situations, and mitigate negative consequences.

The following use cases illustrate spatial analysis applications by EOSDA products (LandViewer and EOS Crop Monitoring) and their actionable insights.

Forest Fires In Colorado

Forest fires in late 2020 turned into a real catastrophe. They ran even a worse scenario due to droughts, hot weather, and high mountainous positions. While usually snow helps manage the issue, the fires rapidly expanded this time (by six times per day).

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The case of the forest fires in Colorado is based on geospatial analysis that includes multispectral data from the optical satellite Landsat-8 and the following band combinations:

  • False color (Urban) with SWIR2, SWIR1, Red is relatively insusceptible to atmospheric effects, going through particles dispersed in the atmosphere.
  • Natural color composites for spatial analytics depict the target as it is perceived by a human eye.
  • Color IR (Vegetation) is typically used in spatial analysis to distinguish between bare soils and healthy/scarce vegetation cover. Broadleaf and/or healthier plants give a more reddish picture, while low-density areas are highlighted in less intensive reds.
  • The Standardized Burning Index NBR in geospatial analysis enables to assess burnt areas smoothing atmospheric influence.

Floods In Colombia

In late October, 2020 six rivers flooded seven municipalities in Magdalena, Colombia, destroying thousands of houses. The affected municipalities include Aracataca, Algarrobo, El Reten, Fundación, Bananera Zone, Pueblo Viejo, and Cienaga.

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The case of the floods in Colombia relies on spatial analysis that incorporates data retrieved from the radar satellite Sentinel-1 and applies the band combinations of VV, VH, VV/VH composites. These composites are efficient to monitor water bodies, moisturized agricultural territories, vegetation, as well as bare soils and urban areas.

Tropical Cyclone Gati In Somalia

Gati has been the strongest tropical storm near coastal Somalia and the first landfall cyclone in the Arabian Sea area since 1970 (when the records were initiated).

The cyclone outraged on November 21-24, 2020 with a landfall in Somali on November 22. The catastrophe was classified as “very severe” by the India Meteorological Department and assigned “category 3” according to the Saffir-Simpson Hurricane and Wind Scale. The fatalities statistics report at least eight persons perished, with dozens missing.

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For a solution to monitor the cyclone and assess its consequences, EOSDA performed a spatial analysis of data acquired by the optical satellite Sentinel-2. To do so, it employed standard color infrared channels for vegetation and the NIR, MIR and IR land/water composites to identify water objects on land and mark the borderlines between land and water.

Rice Field Damaged By Hail

In agriculture, spatial analysis is used for different purposes, including identification of various types of damage by natural anomalies or routine farming activities. The example below shows how the rice field has been damaged by hail. EOS Crop Monitoring software reveals the most visible changes via NDVI and ReCI indices.

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Benefits Of Spatial Analysis

Spatial analysis in GIS facilitates decision making in many spheres from minor routine business issues to global disaster response. The georeferencing technology not only recognizes the coordinates but states the time as well, which allows tracing changes and understanding trends.

While satellites capture remote and hard-to-reach places, spatial analysis software and techniques allow data handling with accuracy and speed that a human analyst would never compete. Computerized spatial data analytics enables nearly instant processing of a huge data bulk.

The obtained results of GIS geospatial analysis highlight the situation from multiple angles:

  • help understand the current state of things;
  • trace tendencies and react correspondingly;
  • develop business strategies analyzing data referred to space and time.

Visualizations in spatial data analysis simplify information perception by rearranging, categorizing, modeling, sorting, and presenting big data in easy-to-remember patterns and enhancing important details.

Thus, spatial analysis provides situation awareness and actionable insights for present and future in any question where geographical position matters.

(Video) Basic Spatial Analysis in GIS

Article tags:

  • LandViewer
  • Remote sensing
Spatial Analysis Of Geospatial Data: Processing And Use Cases (9)

Peter Kogut

Scientist at EOS Data Analytics

Petro Kogut has a PhD in Physics and Mathematics (1998). He successfully defended two dissertations: “Stability and Optimal Stabilization of Neutral Integro-Differential Equations” (1989) and “Stability and Optimal Stabilization of Neutral Integro-Differential Equations, Homogenization of Optimal Control Problems for Systems with Distributed Parameters” (1998).

He is the author of multiple scientific publications, including “Variational Model with Nonstandard Growth Conditions for Restoration of Satellite Optical Images via Their Co-Registration with Synthetic Aperture Radar”.

Dr. Kogut has received two grants: International Fund of Fundamental Investigations - “Vidrodzhennia” (1996) and Ukrainian Fund of Fundamental Investigations (1997).

In 1996, he became the Soros Associated Professor. A year later, he received The First Prize of National Academy of Science of Ukraine for his research in homogenization theory of optimal control problems.

Dr. Kogut has received an honorary decoration, “Excellence in Education of Ukraine” (2014) and the medal of A. M. Makarov, “For significant merits” (2019).

Since 2014, Petro has been the head of the department of differential equations in the Oles Honchar Dnipro National University.

(Video) Spatial Analysis and Data Science

Some of Dr. Kogut’s hobbies include fishing and woodworking.

Dr. Kogut provides scientific advice to EOS Data Analytics.

FAQs

What is spatial analysis as used in GIS? ›

The true power of GIS lies in the ability to perform analysis. Spatial analysis is a process in which you model problems geographically, derive results by computer processing, and then explore and examine those results.

What are the 5 concepts of spatial analysis? ›

Typically, spatial analysis consists of five key stages: understanding your goal, preparing data, choosing suitable tools and techniques, performing the research, and estimating results.

What is geo spatial data analysis? ›

Geospatial analysis is the gathering, display, and manipulation of imagery, GPS, satellite photography and historical data, described explicitly in terms of geographic coordinates or implicitly, in terms of a street address, postal code, or forest stand identifier as they are applied to geographic models.

What is the use of spatial analysis? ›

Spatial analysis allows you to solve complex location-oriented problems and better understand where and what is occurring in your world. It goes beyond mere mapping to let you study the characteristics of places and the relationships between them.

What are spatial analysis techniques? ›

The techniques of spatial analysis can be categorized into six groups: (1) generation of spatial data and its preliminary treatment; (2) spatialization of attribute data and scale transformation of spatial data; (3) visualization and exploration of spatial data; (4) geo-statistics; (5) analysis of lattice data; and (6) ...

What is spatial analysis and its types? ›

Introduction. Spatial analysis is a type of geographical analysis which seeks to explain patterns of human behavior and its spatial expression in terms of mathematics and geometry, that is, locational analysis. Examples include nearest neighbor analysis and Thiessen polygons.

What are the types of spatial data? ›

1.3.

Spatial data are of two types according to the storing technique, namely, raster data and vector data.

What are the examples of spatial data? ›

Spatial data can have any number of attributes about a location. For example, this may be a map, photographs, historical information or anything else that may be deemed necessary.

What are the types of geospatial data used in GIS? ›

There are two primary forms of geospatial data: vector data and raster data.

What are the importance of spatial data in GIS? ›

In GIS, Spatial Analysis is the most useful tool to study spatial data by interpolating them together to bring the information that is important for decision making, visualizing the land use pattern by decade, futuristic study, creating new things like road networks, and buildings and their methods and technologies are ...

What is used for processing spatial data? ›

The most common way that spatial data is processed and analyzed is using a GIS, or, geographic information system.

What are the 3 types of spatial distribution? ›

Spatial distribution can be measured as the density of the population in a given area. The three main types of population spatial distribution are uniform, clumped, and random. Examples of the types of spatial distribution: uniform, random, and clumped.

What are the six categories of spatial analysis? ›

Six types of spatial analysis are queries and reasoning, measurements, transformations, descriptive summaries, optimization, and hypothesis testing.

What plays a key role in processing and Analysing geospatial data? ›

Spatial DBMSs are an essential component of spatial data storage and management for geospatial information processing.

What are the four types of GIS data? ›

The three types of GIS Data are -spatial, –attribute, & —metadata
  • vector data. ...
  • raster or grid data (matrices of numbers describing e.g., elevation, population, herbicide use, etc.
  • images or pictures such as remote sensing data or scans of maps or other photos.

What are advantages of spatial data? ›

With spatial data you can discover growth insights, manage facilities and networks, and provide location information to customer. Without considering spatial components and how they relate to your business, your risks and possibility of poor results will increase.

What are the major components of spatial data? ›

The elements include an overview describing the purpose and usage, as well as specific quality elements reporting on the lineage, positional accuracy, attribute accuracy, logical consistency and completeness.

What is the difference between spatial data and geospatial data? ›

Spatial data, also known as geospatial data, is a term used to describe any data related to or containing information about a specific location on the Earth's surface. Non-spatial data, on the other hand, is data that is independent of geographic location.

What are the four spatial features of spatial data? ›

Important characteristics of spatial data are its measurement level, map scale and associated topological information. Nominal, ordinal, interval and ratio are the four levels of measurement for populating the spatial data matrix; they hold different amounts of information and determine what analysis can be performed.

What are the two spatial data models? ›

There are two broad categories of spatial data models. These are vector data model and raster data models.

What are the two types of GIS data? ›

There are two different types of GIS data, vector data and raster data. Each type of data has its own format.

What are the two main types of ways to analyze GIS data formally? ›

Within the spatial referenced data group, the GIS data can be further classified into two different types: vector and raster. Most GIS software applications mainly focus on the usage and manipulation of vector geodatabases with added components to work with raster-based geodatabases.

What are the 3 tools used to collect geographic data? ›

Geographic information is the collection of information about places and events that occur on the Earth's surface.
...
Digital Globes
  • Google Earth.
  • Google Maps: proprietary data sources.
  • OpenStreetMap: all data have been contributed volunteerarily by citizens uploading and digitizing spatial data.
  • Yahoo Maps.
  • MapQuest.

What is geospatial processing? ›

PLAN offers professional services in the development and application of remote sensing and geographic information technologies (GIS). PLAN provides its services to support the exploration, development and management of the Earth's resources and the monitoring of the environment.

What is spatial analysis simple? ›

Definition from the ESRI Dictionary: "The process of examining the locations, attributes, and relationships of features in spatial data through overlay and other analytical techniques in order to address a question or gain useful knowledge.

What is spatial analysis in GIS PDF? ›

spatial analysis is “a group of methods whose results change when the locations of the objects being analyzed change. For example, calculating the number of locations in a particular domain is spatial analysis because the output is directly. based on the locations. FIG. 3.1 Sampling locations distribution map.

What is spatial analysis in remote sensing? ›

Spatial analysis combines remotely sensed, thematic, statistical, quantitative, and geographical data through overlay, modeling, and other analytical techniques to investigate specific research questions.

What is spatial analysis and its types? ›

Introduction. Spatial analysis is a type of geographical analysis which seeks to explain patterns of human behavior and its spatial expression in terms of mathematics and geometry, that is, locational analysis. Examples include nearest neighbor analysis and Thiessen polygons.

Which type of analysis is done for spatial data? ›

Types
  • Spatial data analysis. Urban and Regional Studies deal with large tables of spatial data obtained from censuses and surveys. ...
  • Spatial autocorrelation. ...
  • Spatial heterogeneity. ...
  • Spatial interpolation. ...
  • Spatial regression. ...
  • Spatial interaction. ...
  • Simulation and modeling. ...
  • Multiple-point geostatistics (MPS)

What are the types of spatial data? ›

1.3.

Spatial data are of two types according to the storing technique, namely, raster data and vector data.

Which is better for processing spatial data? ›

The most common way that spatial data is processed and analyzed is using a GIS, or, geographic information system. These are programs or a combination of programs that work together to help users make sense of their spatial data.

Trajectory is the official publication of the United States Geospatial Intelligence Foundation (USGIF) – the nonprofit, educational organization supporting.

Companies and institutions have come to the awareness that not only must they have access to the right data at the right time, but they must also have access to analysis of the raw data to make correct decisions.. Hence the persistent quest for new ways to use data, find relationships in data, and discover patterns in data.. As we reflect on the uses of geospatial data, one of the most significant growth areas in the broader world of data is the area of data visualization.. Whether rendering information in two or three dimensions, geospatial data is the key to visualizing data, which is why it has become one of the most sought after forms of data.. As we look at how geospatial data is being used in the past and present, it makes us question how the uses of this data will change in the future.. Specifically, geospatial data can provide retailers data on income, housing/rent prices, surrounding business performance, population, and age.. People began using customer data for retail sales forecasting back in post-World War II, however, it wasn’t until the 1990s when our technology improved enough to allow companies to perform “data mining” on their customers and retail stores.. By entering the raw data into data tables and then instructing the GIS software to generate the data into a layer on the map (such as placing pins on the map to mark where a company’s best customers lived), it creates a visual that allows retail companies to recognize certain patterns in the population.. The financial services industry, which traditionally consumes data in the form of dollars, cents, credits, and debts contained within spreadsheets, balance sheets, or financial statements, has discovered value in geospatial data.. By using geospatial data and employing experts in geospatial analysis, companies can access new elements of knowledge, including but not limited to:. Innovation and cutting-edge research and development (R&D) in the field of geospatial data, geospatial science, and analytics continue to yield new ways to incorporate geospatial data into new arenas and offer solutions to today’s most challenging problems.. Geospatial data is a critical element to the operation of drones and small autonomous spacecraft, all of which depend on geospatial data to provide precise positioning.. This expansion indicates that adding geospatial data to any data collection or analysis effort is beneficial.

Get familiar with Spatial Data, Location Intelligence, Heatmap, GeoPandas, and many more topics.

Let us start with the most foundational concept in Geospatial Data Science i.e. Spatial Data.. In Geospatial Data Science, the features reference a geographic location and hence can be put on a map.. Now, Let’s try to analyse some geographically important dataset using GeoPandas.. on some data.. One of the best use cases of location intelligence can be seen in “The Traffic Jam Whopper” by Burger king in Mexico.

Learn the definition of a geospatial database, its fundamentals and uses cases across industry.

A geospatial database is optimized for storing and querying data that represents objects defined in a geometric space, such as vector data and raster data.. For interoperability, geospatial database platforms support standards defined by the Open Geospatial Consortium (OGC), which provide a unified framework and web services—Web Feature Services (WFS) for vector data, Web Coverage Service (WCS) for raster data, and Catalog Services (CSW) used to locate, manage, maintain distributed geospatial data applications and services.. The different layers and types of complex geospatial data ‘Geospatial data’ refers to information about features, objects, and classes on Earth’s surface or even in space.. Examples of point, line, network, and polygon vector data (© 2022 Oracle Corporation ; map data © 2020 HERE)Geospatial raster data is a complex set of information gathered from Landsat satellite enhanced Thematic Mapper (ETM+) sensors, which record light, infrared reflectance value, and their position in the grid.. Raster data types are large and have a very different data structure compared to vector data types.. Raster data sets can grow very quickly, resulting in huge volumes of geospatial information that require data management systems such as Oracle’s spatial database .. An example of raster data (left) and a visualization of 3D data (right) (© 2022 Oracle Corporation ; map data © 2020 HERE)The integration of fundamentally different types of data is one of the central tasks of geospatial data analysis.. Geospatial data can be used to track people who were at the same place at the same time and for how long (for example, COVID track and trace) (left); A visualization of urban planning and development (center); A heatmap and visualization of disease outbreak (right) (© 2022 Oracle Corporation ; map data © 2020 HERE) Lack of integration of spatial data into business processes GIS systems are often dedicated, specialized systems that are disconnected from business systems, which leads to increased training, operations, and maintenance costs.. Heterogenous data Integrated analysis is difficult, as different kinds of data are held in files or specialty data stores, and each needs a specialized skill set.. Scalability Scalability has become a requirement to effectively process ever growing amounts of geospatial data for commercial applications requiring location information, such as sensor data, GPS streaming data, and 3D data.. The geospatial dataflow from data ingestion to processing, visualizing, and finally sharing and publishing results Data ingestion Filter and ingest spatial (shape, size, and location) and nonspatial attribute (name, length, area, volume, population, other) data from various data sources (multivariate data).. Change text data to numerical data and normalize all other numerical data.. Data enrichment enables users to process less structured geographic data so that the information can be categorized, compared, filtered, and associated with other structured data to perform spatial and text analysis.. Geospatial processing Develop spatial analysis workflows, and combine attribute data with geometric datasets, preparing the data for spatial analysis and mapping.

Learn the concepts of spatial data science and how we build Geospatial AI use cases that solve business problems. [CASE STUDIES INSIDE]

Geospatial Data Science, also known as Spatial Analysis or Spatial Data science lies at the intersection of geographical solutions like Geographic Information Systems (GIS) and Data Science.. A 2-minute explanation on Spatial Data ScienceAccording to Gramener’s Senior Data Science Engineer, Sumedh Ghatage, Geospatial Data Science is a subset of data science, that comprises location analytics, satellite imagery, remote sensing, analyzing projection systems, and analyzing raster and vector data .. Some might confuse Spatial Data Science with Geographic Data Science.. Data science uses special characteristics of spatial data in conjunction with other datasets to model answers to these questions.. Since the combination of Geospatial and data science can solve most business problems, enterprises and nonprofits are ramping up investments in Geospatial data science projects.. This data is then fed into data science algorithms to determine the solution to the problem.. Spatial Data Science Use CasesSpatial data analysis is used to understand how cities grow by tracking the growth of buildings and infrastructure.Experts can assign risk scores to areas for planning and recovery in case of environmental disasters.Spatial scientists can track where nature declines by looking at forest area or lake area imagery over time.Spatial data analysis can be combined with health for disease outbreak monitoring and control.Leveraging GIS data science for location intelligence, surge pricing models, marketing, and recommendation systems.Merging spatial data and Landsat data with Machine Learning model to predict crop yield and enable smart farming .The examples listed above just scratch the surface in terms of the possible applications of this emerging technology.. By using several spatial data visualizations and spatial data science tools such as Geopandas, QGIS, PostGIS, Apache Spark, D3, and more we’ve completed successful Geospatial data science projects.. Funded by Microsoft AI for Earth, Gramener’s spatial data science solution predicts population density from satellite imagery.. Identify the region of interest: country and city Prepare the data Execute the AI model Estimate population at a 50X50 meter level Get output imagery with Population by area coverage and areas with no human settlements Grid-wise building area with the number of buildings Spatial clustering of grids

Breaking through th

Mosaic provides:. A geospatial data engineering approach that uniquely leverages the power of Delta Lake on Databricks, while remaining flexible for use with other libraries and partners High performance through implementation of Spark code generation within the core Mosaic functions Many of the OGC standard spatial SQL (ST_) functions implemented as Spark Expressions for transforming, aggregating and joining spatial datasets Optimizations for performing spatial joins at scale Easy conversion between common spatial data encodings such as WKT, WKB and GeoJSON Constructors to easily generate new geometries from Spark native data types and conversion to JTS Topology Suite (JTS) and Environmental Systems Research Institute (Esri) geometry types The choice among Scala, SQL and Python APIs. Mosaic aims to bring performance and scalability to your design and architecture.. This is where indexing systems like H3 can be very useful.. Diagram 9: Mosaic query using index only. Diagram 12: Mosaic query for point index

Chapter 1. An Overview of Geospatial Analytics Geospatial data—that is, data with location information—is generated in huge volumes by billions of mobile phones, sensors, and other sources every day. Data … - Selection from Geospatial Data and Analysis [Book]

Data begets data, constantly ratcheting up the unbounded streams of geospatial data (“geodata” for short) awaiting our analysis.. With the rise of this type of data, the DoD has helped promote open source, open data, and data analysis companies such as Socrata, Databricks, and Uncharted Software.. These layered sensing networks —layers of data sources, ranging from sensors on the ground to vehicles and mobile devices to drones to satellites, all at complementary levels of detail and cost/performance—serve as a flywheel for a tremendous spike in geospatial data (see ).. Whether using ArcGIS or other tools, geospatial work requires atypical data types (e.g., points, shapefiles, map projections), potentially many layers of detail to process and visualize, and specialized algorithms—not your typical ETL (extract, transform, load) or reporting work.. Data sources may be relatively sparse and require statistical smoothing or interpolation (e.g., kriging to convert discrete data points into heatmaps, choropleths, and so on that are more useful to visualize data as geospatial overlays).. In general, the data independence required for data parallel systems (e.g., Hadoop MapReduce) didn’t fit well with geospatial workloads.. We’ll survey the state of play with open source geospatial tools, and we’ll take a look at how some of the biggest of the big (e.g., Google) make use of geospatial data in their various products and services.. As part of our discussion about the Big Data end of the solution scale, we’ll also review the current trends in geospatial innovation, including the state of the art in geospatial analytics platform stacks, and a bit about the mathematics underpinning them.. Near the end of the report we’ll review examples of how geospatial analytics are being brought to bear, and we’ll provide links to additional resources where you can learn more and join the discussion in the geospatial and Big Data communities.

Geospatial technologies are useful in monitoring georeferenced locations. They are widely applied and facilitate decision making in many spheres.

Unlike ordinary spatial data, geospatial technology innovations allow us to determine the exact location of an object or person on our planet.. We apply them in multiple spheres, from geospatial technologies for maps like GPS navigators for drivers to vast-scope remote sensing by satellites orbiting Earth.. Satellites revolve our planet and generate imagery based on several source options and methods of geospatial technology for data collection:. GIS, one type of geospatial technology, merges spatial and non-spatial data, remote sensing imagery, GPS data points to elaborate a single complete system.. Geospatial technology allows tracking a questioned object and referring it to a specific location.. The list includes geography proper, ecology, tourism, marine sciences, agriculture, forestry, marketing and advertising, military forces, navy, aircraft, law enforcement, logistics and transportation, astronomy, demography, healthcare, meteorology, and many others.. Geospatial technologies enhance the performance of artificial intelligence and smart machinery in multiple spheres and agriculture in particular.

Amid the flood of data we collect and contend with on a daily basis, geospatial data occupies a unique place. Thanks to the networks of GPS satellites and

Here are five ways organizations can use geospatial data to fuel analytics in the real world.. One of the leaders in geospatial data is Pitney Bowes , which maintains an extensive catalog of geospatial data, as well as geospatial encoding engines that run on their own or plug-into high-performance databases, such as SAP HANA.. James Buckley, senior vice president and general manager of customer data and location intelligence in Pitney Bowes’ software division, recently discussed several use cases for geospatial intelligence with Datanami.. It’s been delivering that sort of geocoding capability for years, and counts some of the world’s biggest companies as customers.. And you also need a lot of reference data sets, including geocoding, maps, and weather data.. This type of streaming data analytic problem will combine various real-time and historical data sources together.. While such a big data solution will require a lot of computing horsepower, what’s really needed is the right spatial context.. One company at the forefront of this sort of work is Space-Time Insight, a Silicon Valley firm that that helps public utilities, logistics companies, oil and gas firms, and federal agencies to combine and interpret various types of data to maximize the effectiveness of physical assets in the field.. And as you might expect, there’s a big data angle to them, especially in the context of location intelligence and geographic information systems (GIS) products, such as those from ESRI.. Agriculture is poised to be the single largest beneficiary of the combination of UAV and GIS, according to Flightline Geo’s Devon Humphrey, who was featured in a story last year by ESRI News.. Deciding when and where to water, and by how much, is a big part of a farmer’s job, and now Big Blue is bringing big data and location analytics to bear on that problem.. Getting the forecast resolution required to accurately predict those events is really hard, according to Lloyd Treinish, an IBM Distinguished Engineer and Chief Scientist for its Deep Thunder supercomputer.. But to predict pop-up thunderstorms along the Flint, IBM dialed the resolution down to 1.5-kilometer blocks at 10-minute intervals.. But in the retail market, having a geographical element to it can make all the difference in the world.

Spatial analysis refers to modeling location-specific problems, identifying patterns, and assessing spatial data to make decisions. Find out more about spatial analysis and its significance in today’s technological world.

Spatial analysis is defined as the process of studying entities by examining, assessing, evaluating, and modeling spatial data features such as locations, attributes, and their relationships that reveal the geometric or geographic properties of data.. Geometric data maps the spatial information onto 2D surfaces.. It refers to add-on information to spatial data, for example, images, maps, or additional information that adds value to the spatial data of the object under consideration.. For example, spatial analysis can aid in traffic management in an urban setting, thereby allowing authorities to take steps toward building resilient cities.. Spatial analysis is a crucial component of the Geographic Information System (GIS).. Data collection is fundamental for the spatial analysis process.. Examples of spatial analysis include:. For example, measures such as satellite imagery collected via the Landsat program, thermal remote sensing, and field observational studies can aid in developing a better understanding of how the UHI effect is causing a specific spatial pattern.. The program uses machine learning and spatial analysis technology for its operation.. With spatial analysis, one can map regions with direct access to sanitation facilities.. The data can identify the population that does not have access to these healthcare services.. In agriculture, spatial analysis enables crop monitoring, predicting crop yield, soil analysis, and so on.

  Spatial data adds a unique aspect to analysis: the ability to generate insights based on location awareness. Location is one of a small number of universal correlation attributes that apply to...

I have a set of events: earthquakes, and a set of reference data: tectonic plate boundaries.. Clearly if an earthquake occurs exactly on a tectonic plate boundary, it is associated with that boundary.. To find the distance between every earthquake and every boundary requires a spatial join.. We have already seen the first of those in the spatial join: the distance calculation between earthquake and plate boundary.. Those earthquakes will not have a plate boundary name, but it is an easy data transformation to give them a default name using a CASE statement:. The Tableau Public visualization has a slider that allows you to decide how near a tectonic plate boundary an earthquake has to be before it is associated with that boundary.

Videos

1. GIS data models, data acquisition and spatial analysis
(HEAB Geospatial Solutions)
2. Basic Spatial Analysis Geographic Information Systems (GIS): A Technical Video Lecture
(GIScience)
3. Unthrottling your spatial analysis, no matter the scale | Brian Suk & Julie Price | Google
(CARTO)
4. Introduction to Geospatial Data Analysis with Python | SciPy 2018 Tutorial | Serge Rey
(Enthought)
5. Machine Learning in GIS and spatial analysis
(GeoWorld)
6. Data Visualization for Spatial Analysis
(Esri Events)

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