Mod11 - 3D Mapping

3D Mapping

I started this week's work from the assigned ESRI Training Module on 3D Visualization Techniques Using ArcGIS software. The training was done on ESRI web portal. This training module introduced new terms in addition to new techniques such as:

• Triangulated Irregular Networks (TINs), 
• Terrain Datasets, and 
• Multipatches. 

The primary focus was working with 3D features, discrete geographic features such as buildings, rivers, and wells that can be found on or beneath a visible surface. 

I also used ArcScene to display 2D features in a 3D rendering. In the first portion of the training learn how to navigate ArcScene, view 3D data and how to use the Attribute Table's Shape field to determine if the data is a 3D feature class. I then proceed to learned how to set Base Heights for raster data which are set to 0 by default and 3D views enhancement techniques. 

Fig 1. Vertical Exaggeration of 3D layers

In Fig.1, the visual effect of raster elevation data for the Twin Cities of Minnesota was changed by exaggerating the differences in elevation values. the measures values are not altered by executing the  exaggeration technique but the appearance changes. Negative exaggeration has the effect of inverting the data's appearance. Changes in illumination values and background colors  alters the appearance of data. Also changes to the appearance of sunlight's angle/altitude, emphasizes or reduces variations seen on the surface of the 3D rendition. It is also worthy of mention that background colors can make a 3D layer appear to float or provide geographic reference (sky, horizon, water body); a pop effect visually.

Fig.2. Extrusion of Buildings and Wells

In Fig 2., above, I executed extrusion technique to extend features (building and wells) that are above and below the surface. Buildings were extruded above the surface using their Height value while Wells were extruded below the surface using their Depth value. The wells features appeared as flat points on the surface so an offset value was applied to raise them slightly above the surrounding surface. The aerial photo/picture layer in this Fig.2 was draped over the underlying elevation TIN layer, by setting the photo's base height to the elevation values of the TIN.

Fig.3. Extrusion of Land Parcel (showing cost/value of the buildings)

In Fig.3, shows that the z-values does not have to always represent height. The example above shows building parcels in Manhattan, KS that were extruded based on their property value(cost) attribute. Properties with a higher total value were extruded more than lower valued properties. The z-values are not always proportional to their x,y values. Vertical exaggeration was also applied to the features in order for the features to appear proportionally. This was the last training exercise done in ESRI training course for this module.

Fig.4. Extruded Building in Boston viewed via Google Earth

The last phase of this lab exercise was done in ArcMap and Google Earth. Using 3D Analyst tool (Create Random Points) I created Sample Points for 343 buildings in Boston and with these points I performed calculations to determined the mean height for each building which are represented by the z-values. The z-values were used to extrude the buildings in Boston. I then proceed to exported the extruded buildings data in order to create a layer and then converted that layer to a KML file. Finally, the create/exported KML file was opened in Google Earth as seen  in Fig. 4 above.

The extruding buildings such as in the Boston buildings in Fig.4 enables viewers to visually focus on a specific area and view the buildings from different perspectives - above, street level, from different directions - building heights can be compared with relative ease.

Here are other map features created during the course of this lab for your view pleasure.

Fig.5. Elevation Layer draped with Raster (done in ArcScene)

Fig.6. 3D Layer Classified (done in ArcGlobe) 

In conclusion I think it is worthy of note to mention that 3D mapping applications can be used for areal simulation, urban planning, transportation network, real estate, and census to mention just a few. This a fun lab and the result both satisfying and rewarding (visually).

Mod12 - NeoCartography / Google Earth

NeoCartography / Google Earth

This week's focus is on NeoCartography / Google Earth but before I proceed into my gist about this adventure, let me define a couple new terms.

1. NeoCartography (literally "new cartography"): is the new way of both representing and communication geographical information. It is informal and lacks thorough academic data analysis.
2. Volunteered Geographical Information (VGI): is the use of various tools to create, assemble, and disseminate geographic data provided voluntarily by individuals. It is basically a set of user-generated contents.
3. Public Participation Geographic Information System (PPGIS): the inclusion of marginalized population in mapping and GIS academic practices through geographic technology education and participation.
4. Other key terms are Geolocation, Geotagging, Cloud Computing and Cloud-based GIS, I believe these terms are self-explanatory.

Now that I have the definition of the key term learned this week out of the way, the primary objective of this lab is to learn how to navigate/use Google Earth using the South Florida Population Density information from Module 10 as our case study.

To achieve the module's objective, I started my work from ArcGIS where I edited the legend features of "Population  Density Dots" from customed to standard ArcMap depiction of the map features. I then proceed to export two .kmz files ( a zipped version of .kml files) and open it in Google Earth.

Below is a screenshot of South Florida Dot Density map overlayed on a map of Florida from Google Earth.

In Google Earth, I created a fun tour of key locations in South Florida to include but not limited to Miami, Tampa, etc. The tour is not shared in this blog.

 The map above shows downtown Tampa Florida.

This, on the other hand, shows downtown of the city of Miami.

This is one of the most interesting modules. I enjoyed every step along the way and look forward to playing with layers of map features to depict real-life data or phenomena.

Mod2 - Introduction to Graphic Design with Adobe Illustrator

Intro to Graphic Design with Adobe Illustrator

Weeks 2 ushered us into the realm Adobe Illustrator.The objective was to create a basic map using ArcMap featuring Florida Counties, major cites, and bodies of water. After the map was created in ArcMap and exported from ArcMap to Adobe Illustrator with the objective of enhancing the map with Florida State symbols (Seal, State Animals, and Flag).

I explored AI's tools such as the text tool which allows the user to manipulate the text in a variety of ways, color swatches, and shape tools. I also explore importing scripts and creating shapes as well.

The map above depict my lack of proficiency with the software as of a year ago.

Mod7 - Choropleth

Choropleth and Proportional Symbol Mapping

Hi, this week I learned how to create a Choropleth map. My most important take away from this week's lab is the of understanding the data I am trying to present, hence, the need to always review and understand the attribute tables' contents and how they relate to each other.

The first part of the lab involves having an in depth look at the attribute table, which contains the provided data sets for the continent of Europe. Population density and wine consumption are the two primary focus. 

After reviewing the attribute tables' contents, I proceeded to choose how to display the differences in population density across the continent. I chose to use a multi-hued sequential color scheme on Color Brewer and copied the RGB values to create a color ramp. Secondly, I classify the data, opting for the Quantile classification method. The quantile classification method best visually represent the population density of the continent. The Quantile scheme allowed all classes to be visibly represented on the map without any monotone colors confusing viewers/map users as to which class a country belong to. I also group the population density data into five class.

Furthermore, I assessed the wine consumption data for how to best display the data sets. Armed with the fact that: "Proportional and Graduated Symbol mapping are methods of mapping that use visual variables of different size to represent differences in the phenomena, and knowing that Graduated Symbols are best used for data that is classed and Proportional Symbols are best used for unclassified data." I used Graduated Symbols to represent the wine consumption throughout Europe, because Graduated Symbols work best with classed data as opposed to Proportional symbols which work best for raw and unclassified data.

On the color choices, I chose a color scheme that ranges from light yellow/orange to dark red/orange such that my data are represented in sequential order, such as the increase in population density. The lighter colors represented lower data values such as lower population density and the darker colors represented high data values such as higher population densities.

The most challenging part of creating this map was using Adobe Illustrator to create the five different bottles that symbolizes the rate of wine consumption. I found out very quickly that my Illustrator skills became rusty after months of not using the application.

Lastly and most importantly, I learned to always review the content of the attribute table. 

Mod10 - Dot Mapping

Dot Mapping

This week's lab covers the final lesson in thematic mapping. Dot mapping indicates the location of one or more occurrences of a geographic phenomenon using dot symbols. This method of thematic mapping can also be called dot density maps or areal frequency maps. The main characteristics of a dot map is the use of small dots symbolize spatial distributions. The number of dots directly related to the number of objects being mapped.
Conceptual or raw data may be used in creating Dot maps. Dot maps are not used for mapping continuous data but are applicable for:

• discrete data occurring at points, 
• when comparing the distributions of related phenomenon, and 
• when portraying variations or patterns in density.

Dot maps are easy to understand and are effective for showing phenomena that exist in large quantities. Several disadvantages are that they are hard to estimate density and map readers could interpret dots to represent a single occurrence. Dot maps are most commonly used for agricultural maps.

Dot maps can be represented as one-to-one, which is better for smaller geographical areas or one-to-many, which is best for larger geographical areas. 

In creating a dot map, major factors to consider are:

•  the scale of enumeration units, 
• dot value and, 
• dot size. 

Fun map to make but it crashed multiple times and I have forgot to add an insert map the state of Florida that I intended to add.

Mod8 - Isarithmic Mapping

Isarithmic Mapping

In this week's lab I learn about Isarithmic maps which are used to depict smooth, continuous phenomena across an area using different symbology. I also learned about the two different types of data that are best suited for this type of mapping, True Point data and Conceptual Point data. This lab also covered four different methods of data interpolation that are used to derive intermediate points to create the data set, these are: Inverse Distance Weight (IDW), Kriging, Splining, and Triangulation.

This lab assignment was based of the school provided data of precipitation over a 30 year cycle for Washington and the map was designed entirely in ArcMap.

The precipitation data for the State of Washington that was derived and interpolated using PRISM (Parameter-elevation Relationships on Independent Slopes Model) analytical model. This model uses point data and an underlying grid such as a Digital Elevation Model (DEM) to generate grid estimates of monthly or annual precipitation. The precipitation data set was created from the application of this method on point data collected from weather monitoring stations and the calculated climate elevation regression for each grid cell within the DEM. The created data set accounts for physio-graphic factors that may influence climate patterns.

The primary objective of this lab are:

• Define an Isarithmic Map, review different kinds of isarithmic maps
• Identify appropriate data types for Isarithmic mapping
• Recall different interpolation methods (Triangulation, kriging, Inverse Distance Weighted, PRISM)
• Recognize the different symbolization methods
• Implement continuous tone symbology
• Implement hypsometric symbology
• Describe the basics of and create contour lines
• Work with continuous raster data, and
• Employ hill-shade relief

Here's the map rendered:

Hypsometric May with Contour Lines

   The map above used Hypsometric Tint symbology to represent the data with manual classifications f the annual precipitation into 10 classes. A "stepped" or 3-D like surface is created to more easily visually interpret the precipitation differences across the State, with lower values represented by bright/light colors while darker colors represent higher values. 

  This is a fun and color lab.

Mod6 - Data Classification

Data Classification

   This week the focus is on Classification Methods of data sets when rendered in maps. The completed map below is based on the percentage of the population that's above age 65 by census tract for Miami Dade County, FL.

  This map was created in ArcMap using 2010 census data from the United State Census Bureau and other data and instructions from University of West Florida GIS. 

  The five objective of this module are:

1. demonstration of four most commonly used data classification methods.
2. prepare one map with four data frames utilizing ArcMap software.
3. effectively symbolize the map for easy data acquisition/interpretation.
4. compare and contrast classification methods.
5. identify the best suited classification method for spatial data representation.

The classification methods focused on are: Equal Intervals, Natural Breaks, Quantile, and Standard Deviation. I will briefly expatiate on each of the classification methods. 

Equal Interval: The equal interval classification method presents the data by separating it into classes. It divides the total range (Max – Min value) by number of classes to be created. This method is one of the simplest and can be done by hand if necessary. However this method doesn’t take into account how the data falls along a number line and could lead to classes with no values in them. A positive of this method is that there are no gaps in the legend data could lead to confusion.

Natural Break: In this method, data are subjectively broken into classes and as alluded to above, it takes into account where the data is along a number line and tries to group data items based on where they occur most frequently. This tries to keep like values together and unlike values in separate classes.

Quantile : Classifying the data into quantile separates it into numbers of observations per class. This is done through ranking order, e.g. (lowest to highest number) until all data has been equally dispersed among the classes. This does not take into account any data clustering or “Natural Breaks.” Unlike the Equal Interval method there is (are) no chance of an empty class (es) skewing the map.

Standard Deviation : This method of classification does take into account where the numbers lie on a number line. The advantage and disadvantage to this method is that it relies on having normally distributed data. For events with data that follows a Gaussian or standard bell curve this is an excellent choice but, when not dealing with bell curve shaped style of data that contains roughly equal amounts of data points on either side of the mean standard deviation the result will most likely be skewed due to empty classes.

Out of the four classification methods we went over, I prefer Natural Breaks because it best visually represent the statistical data sets. It a fun lab!