Data Visualization

Geospatial Mapping Geospatial mapping is the process of creating visual representations of data that are tied to specific geographic locations. This process helps in understanding patterns, trends, and relationships between data points based on their spatial context.  Here's a step-by-step process for geospatial mapping using a hypothetical example of mapping the population density of different neighborhoods in a city: Data collection and preparation : Gather data related to the variable of interest and its corresponding geographic coordinates. In our example, this would include collecting population data and geographical boundaries for each neighborhood in the city. Ensure the data is accurate, up-to-date, and properly formatted for mapping. Choose a mapping software or tool : Select an appropriate geospatial mapping tool or software, such as ArcGIS, QGIS, or Python libraries like GeoPandas or Folium. The choice depends on factors like complexity, required functionality, and user

  Refer the following link: https://www.statology.org/detrend-data/

  3 key characteristics  of a Time Series Data:  Stationarity, Trend and Seasonality. 1. Stationarity: Stationarity is on demand for almost every time series analysis use case because it is stable to analyze. Moreover, there are useful modeling techniques that require a time series to be stationary, such as Auto Regressive (AR) or Moving Average (MA). So, basically what is stationary and how do we know (or test) if a time series has this characteristic? A strictly stationary time series is one for which the probabilistic behavior of every collection of values is identical to that of the time shifted set. [1] But for most cases, people refer to stationary characteristics with a less formal definition by saying the  mean  and the  variance  of a time series  does not change over time . If you take a shifted sample from an original time series at any lag or lead, you would likely get the same distribution. The former characteristic is known as  strictly stationarity . However in practice,

Time series data A time series is a collection of data points gathered over a period of time and ordered chronologically. The primary characteristic of a time series is that it’s indexed or listed in time order, which is a critical distinction from other types of data sets. If you were to plot the points of time series data on a graph, and one of your axes would always be time. Time series metrics refer to a piece of data that is tracked at an increment in time. For instance, a metric could refer to how much inventory was sold in a store from one day to the next. Time series data is everywhere, since time is a constituent of everything that is observable. As our world gets increasingly instrumented, sensors and systems are constantly emitting a relentless stream of time series data. Such data has numerous applications across various industries. Let’s put this in context through some examples. Examples of time series analysis: Electrical activity in the brain Rainfall measurements Stock