Having decided on a basic methodology for the decision making process, we are now in the process of developing UI mockups, which we can present to stakeholders to get their opinion of the system. UI mockups allow an early view on the look & feel , i.e. usability of the system, and allow the designer to easily collect feedback from users and integrate changes without having to modify/rewrite code.  For my project, I also hope that I can give stakeholders a better understanding of the system development process and of the project’s main aims through this showcase.

I have been looking at different methodologies for producing such UI mockups, and how they have been applied to GIS & SDSS type projects. I must say that I have only found sparse evidence of systematic efforts by GIS practitioners to apply usability principles through the use of mockups  in GIS development activities. My view also is corroborated by Muki in his recent blog post, where he argues that usability and the processes in software development that ensure good usability still seem to be considered a nice to have, and the GIS industry lacks a “usability culture“, inherent other industry sectors.

But I digress…  As always, there are multiple methods of doing UI mockups. One of the most straightforward and widely used methods uses paper, pen and scissors to physically create the UI elements, which can then be arranged into an UI mockup. This of course is very flexible as you can easily create and assemble together whatever user interface elements one needs or wants.A digital equivalent is to use PowerPoint or any other general purpose drawing tool to generate the UI mockup. But there are also drawbacks. Physical UI mockups can get lost/destroyed, are not easy to revise, and changes can’t easily be tracked. Digital mockups made with PowerPoint are also cumbersome to generate, and one needs to invest a lot of time into generating basic UI elements (altough some templates to get you started are available). With PowerPoint mockups, you can even generate basic interactivity inside the mockup by creating links between slides, altough this is still cumbersome.

Obviously, a specific quick and dirty design tool for mockups would be great, and thats exactly what I found with Balsamiq Mockups. They have both a free online tool version as well as a desktop application version available, which supports features such as dynamic links between screens. Also, Balsamiq Mockups intentionally uses hand-drawn UI elements to generate “paper quality mockups“, so that people don’t get attached to “that pretty color gradient” or think that your mockup has actual code behind it and is “practically done”.

I just spent the afternoon creating such a mockup for my project, and I am more than happy with the results, even though they only offer a basic “map” element which is specific to GIS. But the rest of the template of pre defined UI elements is rich enough to model most GIS related UI.

I would urge any GIS developer who hasn’t used mockups for their application development process before to give it a try and enjoy the benefits of better usability for their applications and happier users!

I just wanted to draw attention to this great set of videos that give a great overview of one of the first large scale Geographical Information Systems, in this case the Canada Geographic Information System (CGIS). Dr Roger Tomlinson was the initiator, planner and director of this for the time very ambitious project, which meant that he has gone down in history as the “father” of GIS.  I feel quite proud to note that he wrote his PhD thesis here at University College London!

From the Wikipedia article:

The Canada Geographic Information System (CGIS) was developed in the 1960s and 1970s to assist in regulatory procedures of land-use management and resource monitoring. At that time, Canada was beginning to realize problems associated with its seemingly endless boundaries, in combination with natural resource availability. The government therefore decided to launch a national program to assist in management and inventory of its resources. The simple automated computer processes designed to store and process large amounts of data enabled Canada to begin a national land-use management program and become a foremost promoter of geographic information systems (GIS).

Given that in the short to medium term, we most probably won’t see the integration of significant spatial analysis functionality into Manifold, a pragmatic approach is the integration of external software packages with Manifold. There are a number of software packages that offer spatial analysis and statistics capabilities such as for example Crimestat, Geoda and the R project. R, an open source project benefits from a wide support in academia as a platform for the implementation of statistical computing, and thus provides a very rich environment for the analysis of spatial data through a combination of free packages. R is a command-line environment, and although the syntax is relatively accessible, it does present a significant learning curve for any beginners.

Recently, my research project led me to investigate the spatial distribution of foreign investors into London. I needed to do a density analysis of historic investment patterns to identify likely agglomeration or dispersion processes between investors. Although Manifold doesn’t offer any relevant density estimation algorithms, R, and specifically the spatstat package, allows for the creation of Kernel Density Estimation (for the estimation of density) grids.

I took the opportunity to write a script that gives users a point and click front-end to both the kernel smoothed intensity function from a point pattern (KDE), and spatial smoothing (interpolation) of numeric values observed at a set of irregular locations (GKS) from inside Manifold.  The script takes care of building and maintaining the interface between Manifold and R, running the analysis in the background and creating a result surface component. I must acknowledge here the help and inspiration of numerous users on the forum which have been working with R.

Screenshot example of interface and output

Although R is completely hidden from the user of the script once installed, the successful installation relies on a basic understanding of the concepts of R and the installation of a few prerequisite software tools and R packages. Along with R , you need to have installed R(D)Com A all in one package for R and R(D)Com can be found at statconn. You also need a C:\temp\ directory (temporary files are stored there). You also need to have the following R packages installed:

• spatstat, the main analysis package
• maptools, helper package for the conversion of data to a spatstat compatible format
• rgdal, package allowing the import and export of data from R to Manifold

I strongly advise anyone wanting to use this script to first read and understand the algorithms and outputs involved by consulting the relevant help pages from the spatstat package.  I also include two datasets that are suitable for experimentation with the script, one each for KDE and GKS.

Finally, there are some caveats to this script. I do not make any guarantees as to the output of the script, and I have to repeat that you need to have an understanding of what the algorithms do to fully comprehend the analysis.  Also, the script at the moment doesn’t take account of projections at all. I have personally only tested the script with projected point patterns (British National Grid). In the case of BNG, assigning the BNG projection to the created Image (Preserve Local Values ticked) should be sufficient. Your mileage may vary when using other projections.

You can find the download of the Manifold .map file with the script here!

As you may have guessed, this is only a first stab at the integration of Manifold with R, and it is still an unlikely couple which sometimes can have communication difficulties. Clearly is a lot of work left in integrating other most basic functionality, a few examples of functionalities being other interpolators such as IDW, Geographically Weighted Regression, LISA and Moran’s I … .

But this proof of concept shows the potential for added functionality to boost Manifold’s power from a pure GIS to an exploratory spatial statistics toolset.

HeatMapApi.com is a new service which allows Google Maps mashups to integrate heat map representations easily. Heat maps,  or more generally point to raster interpolations allow the graphical representation of point patterns through the use of continuous colors identifying areas of higher or lower density of points. Areas where this has been employed are crime hotspots analysis or economic activity analysis.

A novel concept in Web 2.0 mashups, I was interested in finding out what the methodology was behind the generation of these rasters. Sadly, I couldn’t find a definitive answer on the algorithm that the website uses to generate its hotspot maps. They do expose in their API two variables that can influence the generation of the heatmaps, decay and boost, but without information on the algorithm behind it, the setting of these values remains a pure exercise in trial and error, and seeing what “looks” best. Also, because the parameters are set as “optional”, most developers will be tempted into a one size fits all approach, smoothing out interesting patterns in the data, or creating hotspots that are not statistically viable, creating masses of effectively meaningless maps.

Mashup developers thus will more than ever the spatial analysis literacy skills to understand the processes, models and algorithms that lie behind the pretty maps.

Note: This is not a new problem, but has been present all through the development of Exploratory Spatial Data Analysis over the past 20-30 years in academia and commercial settings, and a lot can be learned from this past experience.