Spatial Reasoning


Learning from and improving upon ggplotly conversions

@machinelearnbot

There are also a bunch of other R packages that, like albersusa, make it easy to query geo-spatial data as an sf data. The "Reverse dependencies" section of sf's CRAN page is a good place to discover them, but just to name a few: tidycensus, rnaturalearth, and mapsapi. The most brilliant thing about sf is that it stores geo-spatial structures in a special list-column of a data frame. This allows each row to represent the real unit of observation/interest – whether be a polygon, multi-polygon, point, line, or even a collection of these features – and as a result, supports workflows that leverage tidy-data principles3. Moreover, sf tracks additional information about the coordinate system and bounding box which ensures your aspect ratios are always correct and also makes it easy transform and simplify those features (more on this later).


Visualizing geo-spatial data with sf and plotly

@machinelearnbot

Work with me or attend my 2 day workshop! You might be wondering, "What can plotly offer over other interactive mapping packages such as leaflet, mapview, mapedit, etc?". One big feature is the linked brushing framework, which works best when linking plotly together with other plotly graphs (i.e., only a subset of brushing features are supported when linking to other crosstalk-compatible htmlwidgets). Another is the ability to leverage the plotly.js API to make efficient updates in shiny apps via plotlyProxy().


The Future is in IoT, AI, Robotics – Rajesh Alla, IIC Technologies

#artificialintelligence

Our lives today, and in the future, will necessarily pivot around the digitization of objects in the universe, through the efficient land, sea, and aerial surveys. The data collected will embed locational intelligence that will help us create maps with enhanced and meaningful spatial properties. These maps will form the substrate upon which the DNA of physical objects and their thematic properties will be seamlessly interwoven. The resulting rich datasets will become amenable to real-time analysis through Cloud computing that can be shared anytime, anywhere! Temporal resolution of the data is going to be crucial for real-time and near-real-time applications and thus controlled crowdsourcing with automated validation tools is bound to lead to more opportunities.


Clustering to Reduce Spatial Data Set Size

arXiv.org Machine Learning

Traditionally it had been a problem that researchers did not have access to enough spatial data to answer pressing research questions or build compelling visualizations. Today, however, the problem is often that we have too much data. Spatially redundant or approximately redundant points may refer to a single feature (plus noise) rather than many distinct spatial features. We use a machine learning approach with density-based clustering to compress such spatial data into a set of representative features.


Analyzing Geographic Data with QGIS - Part 1

@machinelearnbot

Today I'm writing this post to explain how it's possible to make geographic analysis and answer questions like: which is the richest area in my city? How many people do live in one neighborhood?


How businesses are leveraging location, business intelligence and spatial analytics?

#artificialintelligence

Location is a unifying theme for businesses. Location can be an address, a service boundary, a sales territory, or a delivery route. Spatial relationships, patterns and trends reveal invaluable business intelligence and bring easy-to-understand visualization to business applications.


Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks

arXiv.org Machine Learning

An important problem in geostatistics is to build models of the subsurface of the Earth given physical measurements at sparse spatial locations. Typically, this is done using spatial interpolation methods or by reproducing patterns from a reference image. However, these algorithms fail to produce realistic patterns and do not exhibit the wide range of uncertainty inherent in the prediction of geology. In this paper, we show how semantic inpainting with Generative Adversarial Networks can be used to generate varied realizations of geology which honor physical measurements while matching the expected geological patterns. In contrast to other algorithms, our method scales well with the number of data points and mimics a distribution of patterns as opposed to a single pattern or image. The generated conditional samples are state of the art.


Spatial Data Analysis with R Boot Camp Udemy

@machinelearnbot

Data Science is one of the hottest jobs of the 21 century with an average salary of over $120,000. This course is designed learners of all backgrounds including beginners with no programming experience to experienced programmers who would like to advance to become a spatial data scientist.


Maps and the Geospatial Revolution Coursera

@machinelearnbot

About this course: Learn how advances in geospatial technology and analytical methods have changed how we do everything, and discover how to make maps and analyze geographic patterns using the latest tools. The past decade has seen an explosion of new mechanisms for understanding and using location information in widely-accessible technologies. This Geospatial Revolution has resulted in the development of consumer GPS tools, interactive web maps, and location-aware mobile devices. These radical advances are making it possible for people from all walks of life to use, collect, and understand spatial information like never before. This course brings together core concepts in cartography, geographic information systems, and spatial thinking with real-world examples to provide the fundamentals necessary to engage with Geography beyond the surface-level.


Spatial representations of self and other in the hippocampus

Science

An animal's awareness of its location in space depends on the activity of place cells in the hippocampus. How the brain encodes the spatial position of others has not yet been identified. We investigated neuronal representations of other animals' locations in the dorsal CA1 region of the hippocampus with an observational T-maze task in which one rat was required to observe another rat's trajectory to successfully retrieve a reward. Information reflecting the spatial location of both the self and the other was jointly and discretely encoded by CA1 pyramidal cells in the observer rat. A subset of CA1 pyramidal cells exhibited spatial receptive fields that were identical for the self and the other.