What?

The gathering, displaying, and manipulating of location-based information, or geodata, such as GPS coordinates, street addresses, zip codes, and satellite pictures, is known as spatial analysis. By superimposing layers of location and business data, such as sales statistics or transit times, on maps, geospatial analytics, can help you discover location-based insights. This allows you to visualise, analyse, and gain a more comprehensive perspective of your data. It is also known as geoanalytics.

WHY?

Location analytics gives you information on the “where” parts of your business and can assist you in making decisions about everything from where to locate a new bank or retail outlet to forecasting the most effective travel routes. Additionally, it enables you to observe variations over time, such as seasonal changes in customer spending or how the weather affects delivery delays. Additionally, you may more quickly examine and spot trends and obtain insights that you would have missed from data displayed in tables or columns since geospatial analytics makes this data available through visual mapping.

Finding ways to employ spatial analysis to boost sales, reduce expenses, and strengthen core skills will enable you to boost ROI and strengthen your competitive advantage.

HOW?

Understanding your purpose, gathering data, selecting appropriate tools and procedures, doing the research, and estimating findings are the five main stages of spatial analysis. First, decide what it is that you are interested in learning. The choice of appropriate spatial analytic techniques for data modification and interpretation will come next. You can adapt the research strategies to your needs if you are clear on what you want to unveil. After making a decision, you begin the following phase of spatial analysis, which is the data processing and interpretation stage. Finally, determine whether you achieved the goal or not by estimating the results. 

PREPROCESS:

• Geometric Rectification: Geometric correction for spatial data analysis offers geographic reference of images and improvement of geometric inconsistencies due to a number of factors that match differently for different types of satellite imagery. Such factors are uneven Earth’s surface, changing distance between Earth & satellite orbits, rotational & revolutionary movements of Earth etc.

• Spectral Rectification: Initial satellite imagery contains the supposedly “raw” brightness data (digital numbers). A proper comparison of photos retrieved from diverse sources is not possible with this data format. Due to this, digital quantities in spatial analysis are converted to physically meaningful units, such as actual surface reflectance or emittance values, using spectral (or radiometric) correction.

• Radiometric Atmospheric Rectification: The quality of the image is also affected by atmospheric factors that reduce the signal strength coming from both the sensor and the target. The primary factors affecting pixel brightness and necessitating further adjustment are sunlight absorption and cloud cover, as well as scattering from atmospheric particles (such as dust, mist, fog, carbon dioxide, and methane).

• Missing Pixel Recovery: Due to system faults during data gathering or transmission, condensation etc., certain information required for geographic analysis may be absent. The most popular technique for recovering dropped lines in spatial data analysis is using pixels from nearby lines or averaging the two. 

• Image Contrast Enhancement: The difference between the minimum and maximum luminance, or brightness and colour saturation, in an image allows the subject’s outlines to be distinguished from the background. Fixing low contrast is a common problem. With visual imagery decoding, outlines for spatial analysis and modelling in particular can be more clearly defined.

• Image Filtering: By modifying the sliding window and recalculating and assigning new pixel values, filtering in spatial data analysis highlights necessary items and eliminates noise. The updated values are derived mathematically from surrounding pixels. 

WORKFLOW:

1. Data Collection: Data collection is essential to the process of spatial analysis. It comprises data collection from a variety of sources, including airborne systems and remote sensing tools like LiDAR (light detection and ranging). Such devices collect data that is used to create maps that indicate the geographic distribution of the objects being studied, such as a map of regional temperature variations. High-resolution images or pictures captured by satellites or other aerial systems are considered data in this context.

• Data Analysis: The acquired data is examined in the second stage utilising AI and ML tools to produce findings. Additionally, by analysing millions of photos, one may train ML models to find items or structures in a given area. Institutions, parks, traffic areas, populated areas, etc. are a few examples of objects. Additionally, one can highlight various things using visualisation tools by changing their colours, forms, or annotations. These techniques make it easier to identify objects in huge data sets.

• Data Presentation: The display of post-analysis data can take some time because it is necessary to draw attention to important details that reveal the results. Data visualisation technologies that use tables, charts, and graphs to present pertinent data and interact with concerned stakeholders make these duties simpler. Additionally, 3D visualisation tools provide a greater viewpoint and add variables to 2D data. By optimising planning and implementation techniques, these practises produce better answers to the modelled problems.

CRITICAL CAPABILITIES:

• Geographic Research: One can visualise certain data on maps using interfaces and spatial analysis. Through dashboards, the user may do relevant geographic data searches using terms like city name, nation, zip code, etc. With such a search tool, it is simple to locate important locations in a community, like nearby hospitals, hotels, rail stations etc.

• Data Clustering: By clustering data through geographical analysis, authorities can better comprehend demographic trends by examining the distribution of projected data points. Maps, for instance, can be used by police authorities to determine how far apart two police stations are in a given area. Such information can be used to determine if certain places have easy access to school facilities or not.

• Comprehensive Data View: A bird’s eye view of a region can be obtained by utilising a variety of colours, shapes, and annotations. For a complete perspective of the geographic data, the map can be marked and labelled differently for hospitals, colleges, markets and bus terminus for instance.

• Visual Mapping: Layers, like those seen in heatmaps or bubble charts, can be used by users to represent datasets on maps. For instance, weather information may be displayed on various layers to aid with visual mapping.

• Target Highlighting: By merging the data projected on the layers in the map, many forms of data can be shown on straightforward graphs. For instance, you could overlay real-time weather data with weather warnings to determine how those patterns effect airport traffic in a specific region or connect road geography with traffic conditions to study traffic flow in a specific area.

BENEFITS:

GIS spatial analysis enables decision-making in a variety of contexts, from minor everyday company concerns to international crisis response. The georeferencing technology not only recognises the coordinates but also provides the time, enabling the analysis of trends and the ability to track changes. In contrast to how quickly and accurately a human analyst could handle data, spatial analysis tools and methodologies enable satellites to record distant and difficult-to-reach locations. An enormous data bulk may be processed almost instantly thanks to computerised spatial data analytics.

CONCLUSION:

Although spatial data, such as satellite images, have been around for a while, it has been difficult to sort through the enormous amounts of data to provide useful information. Spatial analysis has, however, become much more accessible with the rise of artificial intelligence (AI) and machine learning (ML), enhancing analytical dimension. 

Spatial analysis has become a crucial component of our daily lives in the modern world. Spatial analysis is now a pervasive technique and numerous fields spanning disciplines, including healthcare, astronomy, banking, forestry, supply chain, government and non-government organisations, and many others, now inextricably include spatial analysis.

Complex spatial algorithms are used by central and state organisations to assess geographic data and develop implementation plans for programmes. Additionally, this data-backed strategy guarantees programme success, which is crucial when it comes to issues of public welfare.

Organizations can create fixed plans using spatial analysis technology that optimise their total finances and guarantee that the programmes are beneficial to the greatest number of people.