For a recent project we were provided with a GIS shapefile of developments (primarily houses) in a suburban neighborhood in Ithaca, NY. We created static maps to show change through time with color coded dots for different time windows but we thought that the dataset was a perfect opportunity to show off CartoDB’s time series […]
Continue reading →ggplot2When you’re plotting a lot of data overplotting can sometimes obscure important patterns. In situations like this it can be useful at the exploratory data analysis phase to ‘jitter’ the data so that underlying data can be viewed making it easier to see patterns. In addition, jittering can be a way of ‘anonymizing’ spatial data […]
Continue reading →CartoDB is a great service for generating online maps and is particularly useful if you’re working with big data that might not render quickly using APIs such as Leaflet and Google Maps. For details on CartoDB we did a write-up of the nice functionality here. In working with CartoDB layers, though, one of the challenges […]
Continue reading →Although many data science-related projects can be completed with a single software tool we often find that decisions about what tool to use for a project involve weighing a combination of what tool would be “best” for the job, what tools we're most familiar with and whether we already have scripts we can use. As […]
Continue reading →For a current project we needed to render, on the web, a geographic file with approximately six thousand features. The shapefile itself is approximately 11 megabytes, far too big to be handled speedily in a web application. We were planning to render the features using GeoServer and map tiles but decided to investigate slimming down […]
Continue reading →leafletR packageIn a recent post (which you can find here) we identified the first publish date for all spatial packages listed in the Analysis of Spatial Data Task View on the R website. The most recent of these, published in March 2014, is the leafletR package by Christian Graul. We were surprised and impressed that, if […]
Continue reading →rChartsData Driven Documents, or D3 for short, is an incredible JavaScript library for creating interactive data visualization on the web. Earlier this year, for example, we illustrated the power of D3 by interactively linking maps and charts in this visualization. D3, however, can be challenging to work with, especially if you don't have experience with […]
Continue reading →In mid-March, 2014 Google announced that it added support for GeoJSON to the Google Maps API (v3). Although other mapping APIs, namely Leaflet, have supported GeoJSON data for quite some time, easy access to GeoJSON in Google Maps will simplify the coding lives of many developers who often use the Google Maps API. In the […]
Continue reading →We use PostgreSQL/PostGIS to manage a lot of our tabular and geographic data from the US Census. In terms of workflow we will either download a shapefile manually from ftp://ftp.census.gov/geo/tiger/ or, if we’re dealing with more than one file (block groups or blocks for example), we will do this from within R (using the download.file() […]
Continue reading →ggmap PackageOne of the great pieces of the new ggmap package is the geocoding functionality. Other R functions can be used to geocode but they fail to provide detailed output like geocode accuracy which is often critical. You need to know if the lat/long in the output refers to a rooftop location or a city center, […]
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