Jyothi Gupta's blog

Outline Spatial Analysis basics – Geometric Properties: Calculating Distance, Area, Shape Generalisation and Centroids Spatial analysis concepts:  • Topology • Clip, Merge, Intersect Spatial Analysis “Spatial analysis or spatial statistics includes any of the formaltechniques which study entities using their topological, geometric, or geographic properties.” – Wikipedia (slaps wrist...!) Spatial analysis is a set of methods whose results are not invariant under changes in the locations of the objects being analysed. Spatial Analysis (simple or complex)can make what is implicit explicit. We do not look at analysis of attribute now. Geometric properties are associated the shape, size and relative positions of objects Topological properties relate to the elements of an entities geometry that remain unchanged if you alter their shape, size etc. Geographic properties relate to an entities place on the earth – the importance of location Distance Calculating Distance on a National Grid Ve...

Outline The importance of pattern Patterns of categorical point data – Point Pattern Analysis Quadrat Analysis Ripley’s K DBSCAN Patterns of spatially referenced continuous observations Spatial autocorrelation Defining near and distant things Measuring spatial autocorrelation Moran’s I Geary’s C LISA The importance of Patterns Need to disentangle the influence of space. It may be of specific interest, or need to be removed.  The human eye/ brain is notoriously hard at spotting patterns!! Quantifying Spatial patterns There are a number of techniques in spatial analysis which can allow us to quantify spatial patterns… Some techniques are more appropriate for understanding the spatial distribution of discrete objects or events of a categorical nature, e.g. Cholera Deaths; Blue plaques; Trees; Post boxes; Burglaries, Breweries in London etc. Often these events are recorded as points and as such the techniques fall under banner of: Point Pattern Analysis Here the properties are fixed, ...

Overview Definition, and relationship to geographic representation Conception, measurement and analysis Vagueness, indeterminacy accuracy Statistical models of uncertainty Error propagation Living with uncertainty Biases and Statistical Fallacies Are these true? Why? If nothing change during an intervention, the results shows the impact of intervention If something is good, then more is better If I get too many tails, I should get a head quite soon Good-looking people are self-centered ... When I review a paper or dissertation or thesis, these are some of the stuff I look for! Tada, I can play fun game in viva! These can happen to every project and just need a bit of attention. They all are to do with uncertainty, biases and fallacy. Introduction Our world is too big and complex to be measured, studied, modeled, represented, and predicted with zero level of uncertainty Truth value is unknown! Measurements are not perfect (issues with quality and biases) What do we mean by 'uncertai...

Conducting Reproducible Research by R MarkDown and Github Why? From rOpenSci “The aim of practising reproducible computational research is to expose more of the research workflow to our audience. This makes it easier for them to make a more informed assessment of our methods and results, and makes it easier for them to adapt our methods to their own research.” http://ropensci.github.io/reproducibility-guide/sections/introduction/ Guards against Academic fraud Erroneous interpretation  Allows for Independent verification through transparency Critique of methods Critique of results  Promotes New techniques Good scientific practice How to we produce reproducible research? Lots of different ways, but in Geographic Information / Spatial Data Science, two tools – Markdown and Github – are making openness and reproducibility far more straightforward RMarkdown RMarkdown documents enable the integration of text (commentary) with code and the outputs of that code. RMarkdown documents ca...

Outline of Cartography and an Making Good Maps Why making maps is hard  Parts of a map Good cartographic practice General principles The consequences of maps Making Maps is Hard! The combination of good analysis and good visualisation. One without the other makes for a bad map. Poor analysis with good visualisation is probably more dangerous. It is very easy to make a very bad map! QGIS is particularly good for helping you decide on the breaks in your data – Natural Breaks https://censusgis.wordpress.com/students/lesson-5- visualisation-cartographic-practice/ Making Maps is Hard: Krygier and Wood’s Checklist What is the map trying to accomplish? Do you really need a map? Is the map suited to the audience? Have you included sufficient attribution information for data sources etc.? What are the likely impressions of the map? Are the data appropriate for the map’s purpose? Does the symbolisation reflect the character of the phenomenon/ data? Is the level of generalisation appropriate?...

Representing the world from infinitely complex reality to models and representations Outline - what is representations, digital representations, Discrete objects and fields, Rasters and vectors and Projections. Representations Representations are needed to convey information. They are need to fit information into a standard form or model. In burglar diagram the coloured trajectories consist only of a few straight lines connecting points. If we looked closer we would reveal more information. They almost always simplify the truth. Accuracy of Representations Representations can rarely be perfect. Details can be irrelevant, or too expensive and voluminous to record. Its important to know what is missing in a representation. Representation can leave us uncertain about the real world. Digital Representation At its root uses only two symbols, 0 and 1 to represent information. The basis of almost all modern human communication. Many standards allow various types of information too be expresse...

Topic: What is Artificial Intelligence? What is its significance for business organizations? Explain how it is used by one such UK based organization of your choice. Provide appropriate examples to support your content.  Artificial intelligence (AI) is the buzz word these days in our present industries. In simple words, AI could be defined as human to machine interaction using computer application. Although, we don’t have any predefined definition for AI, but we predict that with new and improvised technology we could create a higher standard and more secure standard of living for human being. With help of technology, we could work intelligently and have a virtual future with robots, IOT (Internet of things) and Data science. However, with this automation processes, we do have a major significance on our business organizations. Firstly, we increase competition and improve efficiency. Secondly, clients expect to have a advanced solution with use of AI, IOT and machine to human inter...

Geospatial Information is multidimensional (x,y), oftenprojected onto a flat surface, voluminous, representable atdifferent resolution/scale, with a lot to reveal about us! Special methods are required to analyse geospatial data Procedures are usually complex and expensive (even if static) Retrieval of large amounts of data for each analysis (even display), and long transactions for data manipulation requires special database, software, hardware GISystem Can have the following architectures Desktop GIS Client Server  Distributed Web GIS Client-Server Architecture (Desktop GIS + Network + Data Server) 3 Logical Layers in many Physical machines  Many clients and one server (data server) Advantages: Consistency of data (there is only one version of data) Many Users (100 for example; usually below 300 due to limited connection objects) Distributed Architecture (Desktop GIS + Network + Data Server + Application Server) 3 Logical Layers in many Physical machines Many clients and two...