Lab 4 - ArcCollector and Data Sharing


The purpose of this lab was to collect field data using ArcGIS Collector and then use the collected field data to explore several methods to create and share the resulting maps: ArcGIS Online map, story map, Google Earth KMZ web map and map package. 

I chose to collect field data on fire hydrants on my running route. Before heading out to collect data, I created a new feature class with a custom attribute table and a "Condition" domain that included a list to assess the condition of the hydrants. I uploaded the feature class to ArcGIS Online and  created map. I downloaded the ArcGIS Collector on my phone and used it to survey the fire hydrants during my run. I found the ArcGIS Collector App easy to use. My domains were set up correctly and I could choose from the list I created. I was also surprised that the Date field automatically allowed selection from a scrolling list. I was worried that I would have to enter the date manually and I could see how that would create issues with inconsistency in how dates were entered, particularly with multiple users. My only issue was with the order of the fields. It took a few moments to find the Date and Notes fields since they were at the bottom of the list and I had to scroll to find them. I would change the order and see if that made a difference in future surveys. This app would be useful in a variety of situations and be particularly helpful in increasing efficiency and reducing duplication of work. The field data automatically populated the ArcGIS Online map, which included links to pictures I took during the survey. 

I explored other ways to share the map features, other than simply sharing the ArcGIS Online map. I was also able to view the ArcGIS Online map through the Portal on ArcGIS Pro and create a map package that I shared with UWF. This package is editable by the users with whom the package was shared. In addition, I created KMZ files from the ArcGIS Online map and ArcGIS Pro maps and uploaded these to Google Earth on a web map. The difference between these two methods of creating KMZ files is that the pictures were not included in the file created with ArcGIS Online. Although the color symbology was retained in the Google Earth map, there was no legend explaining the colors. There was also limited functionality in terms of editing the features in the Google Earth map - features could only be renamed and deleted. Finally, I created and shared a story map from the ArcGIS Online map: http://arcg.is/1CDzD90. Of all the methods used to share the field data, the story map had the least functionality in terms of modifying the data. The story map is only for sharing and viewing information. The method used to share data will be determined by the purpose of the share: the extent that the data need to be modified.

On a personal note and something interesting from my surveys: the hydrants changed color and shape during my survey. My route crosses into another county. Apparently the hydrants are different in the two counties. The hydrants are not very far apart and if you look at the pictures on the map, you can clearly see the hydrant differences.

Comments

Post a Comment

Popular posts from this blog

Module 6: Proportional Symbol and Bivariate Choropleth Mapping

Image
The objectives of this week's lab was to explore proportional symbol and bivariate choropleth mapping techniques, as well as customizing legends for each technique. One of the data sets for this lab included positive and negative values. The final map utilizes proportional symbols to show both positive and negative values. This map is essentially two proportional symbol maps overlaid on each other, one for positive job growth and another for negative job growth, with the symbols for both variables sized the same. Utilizing proportional symbols to show both positive and negative values is an effective way to visualize areas that show the greatest and lowest values. These areas are easy to see quickly on the map and give the map reader a simple way to visualize the variable being mapped. Fig 1. Proportional symbol map using positive and negative values.  Another data set for this lab examined contained two data variables: percent of the US population in each state that is obe
Read more

Module 2: Coordinate Systems

Image
In this week's lab I explore different coordinate systems and projections in order to better understand the nature of distortions introduced by the systems. For the last part of the lab, I was tasked with selecting a state in the contiguous US and selecting an appropriate projection for the area. I selected the state of Wyoming as my area of interest. Wyoming has 4 State Plane zones and two UTM zones, so neither a single State Plane nor UTM system would work for this area without distorting the size or shape of the state. Therefore, the best coordinate system for this area is Custom Coordinate system. I chose NAD 1983 WyLam (Meters), a custom system specifically for the state of Wyoming, as the projected coordinate system. This coordinate system is a Lambert secant-case normal-aspect conic type projection which preserves shape and is appropriate for a general reference map. The standard parallels are placed at 41N and 45N, the boundaries of the state of Wyoming, which minimizes
Read more

Module 4: Color Concepts & Choropleth Mapping

Image
In this week's lab, I explored using color and selecting color ramps in ColorBrewer, as well as applying these concepts to create meaningful choropleth maps. I created a map of population change from 2010 to 2014 in Georgia counties. I normalized the data and produced a choropleth map of population change using a diverging color scheme with 6 classes divided around a midpoint of 0. Fig 1. Choropleth map of population change in Georgia counties using a diverging color scheme. For the Georgia population data set, I chose to divide the data set into 6 classes around a midpoint of 0: 3 showing a decrease in population and 3 showing an increase in the population. The classes were grouped based on a manual classification closely based on the Natural Break classification, where the midpoint was adjusted to 0 with the other groups unchanged. Natural Breaks classification creates classes based on similarities in values in the data, thereby revealing patterns in the data. By adjust
Read more