This paper presents Generalized Correspondence-LDA (GC-LDA), a generalization of the Correspondence-LDA model that allows for variable spatial representations to be associated with topics, and increased flexibility in terms of the strength of the correspondence between data types induced by the model. We present three variants of GC-LDA, each of which associates topics with a different spatial representation, and apply them to a corpus of neuroimaging data. In the context of this dataset, each topic corresponds to a functional brain region, where the region's spatial extent is captured by a probability distribution over neural activity, and the region's cognitive function is captured by a probability distribution over linguistic terms. We illustrate the qualitative improvements offered by GC-LDA in terms of the types of topics extracted with alternative spatial representations, as well as the model's ability to incorporate a-priori knowledge from the neuroimaging literature. We furthermore demonstrate that the novel features of GC-LDA improve predictions for missing data.

Spatial Visualization Using R: One of the less understood aspects of R is in spatial data visualization. The below article will outline two case studies on using R to spatially visualize data. Our first step is figuring out how to use the Census API within R. Given below are the key data Source Details from the Census ACS Data We use the acs.lookup function & use the keywords to find the required data across all ACS tables. For example, the following are the search results for the keywords owner, occupied, and median. Using the Choroplethr package make it really easy to create thematic maps in R.

He takes the NYC Data Science Academy 12 week full time Data Science Bootcamp program from July 5th to September 22nd, 2016. This post is based on their first class project - the Exploratory Data Analysis Visualization Project, due on the 2nd week of the program. You can find the original article here. Airbnb boasts almost two million listings in 34,000 cities, and according to data from Inside Airbnb, a independent data analysis website, listed about 36000 apartments in New York as of July 5, 2016. This data exploration sets out to visualize how Airbnb operates in New York City.

Codified narrative is the product of converting human-friendly narrative into computer-friendly code. In past blogs, I discussed my own approach towards this process of codification. Here, I will be covering the idea of spatial, temporal, and contextual distribution of codified narrative. I have never suggested that narrative can or should be used in place of quantitative data. However, I have reflected on how the quantitative regime has tended to dominate discourse; this has perhaps led to data being contextually constrained or deprived. Geography is a type of context that can shape the extent to which people interact with the world. Space provides a medium to distribute resources. It can be involved in forced confinement. An office full of cubicles demonstrates control and dominance over space.

With dozens of public satellites in orbit and many more scheduled over the next decade, the size and complexity of geospatial imagery continues to grow. It has become increasingly difficult to manage this flood of data and use it to gain valuable insights. That's why we're excited to announce that we're bringing two of the most important collections of public, cost-free satellite imagery to Google Cloud: Landsat and Sentinel-2. The Landsat mission, developed under a joint program of the USGS and NASA, is the longest continuous space-based record of Earth's land in existence, dating back to 1972 with the Landsat 1 satellite. Landsat imagery sets the standard for Earth observation data due to the length of the mission and the rich data provided by its multispectral sensors.

This paper presents a new terrain analysis algorithm for RTS games. The proposed algorithms significantly improves the analysis time of the state of the art via contour tracing, and also offers better chokepoint detection. We demonstrate that our approach (BWTA2) is at least 10 times faster than the commonly used BWTA in a collection of StarCraft maps. Additionally, we show the usefulness of terrain analysis in tasks such as pathfinding and discuss potential applications to strategic decision making tasks.

Airbnb boasts almost two million listings in 34,000 cities, and according to data from Inside Airbnb, a independent data analysis website, listed about 36000 apartments in New York as of July 5, 2016. This data exploration sets out to visualize how Airbnb operates in New York City. Airbnb's presence in NYC has been clouded in controversy from the beginning, with law makers arguing that Airbnb drive up rents for New York residents, as well as facilitating a lot of illegal hosting activities, all the while not paying any of the fees hotels are subjected to. Rent is drived up when landlords decide to rather rent apartments to short-term guests at higher rates, compared to signing up tenants for yearlong leases. In a study conducted in 2014, The New York State Attorney General concluded that 72%of all units used as private short-term rentals on Airbnb during 2010 through mid-2014 appeared to violate both state and local New York laws.

This paper describes a series of new qualitative spatial logics for checking consistency of sameAs and partOf matches between spatial objects from different geospatial datasets, especially from crowd-sourced datasets. Since geometries in crowd-sourced data are usually not very accurate or precise, we buffer geometries by a margin of error or a level of tolerance, and define spatial relations for buffered geometries. The spatial logics formalize the notions of `buffered equal' (intuitively corresponding to `possibly sameAs'), `buffered part of' (`possibly partOf'), `near' (`possibly connected') and `far' (`definitely disconnected'). A sound and complete axiomatisation of each logic is provided with respect to models based on metric spaces. For each of the logics, the satisfiability problem is shown to be NP-complete. Finally, we briefly describe how the logics are used in a system for generating and debugging matches between spatial objects, and report positive experimental evaluation results for the system.

He and his team are focused on optimizing C2FO's capital markets through applied machine learning and developing contemporary quantitative risk management systems. Piero also enjoys teaching, rowing, and hacking on open data. You can find him on Twitter and LinkedIn. Geospatial analysis is a massive field with a rich history. Python has some pretty slick packages for working with geospatial data such as, but not limited to, Shapeley, Fiona, and Descartes.

In this study, we present and analyze a framework for geometric and topological estimation for mapping of unknown environments. We consider agents mimicking motion behaviors of cyborg insects, known as biobots, and exploit coordinate-free local interactions among them to infer geometric and topological information about the environment, under minimal sensing and localization constraints. Local interactions are used to create a graphical representation referred to as the encounter graph. A metric is estimated over the encounter graph of the agents in order to construct a geometric point cloud using manifold learning techniques. Topological data analysis (TDA), in particular persistent homology, is used in order to extract topological features of the space and a classification method is proposed to infer robust features of interest (e.g. existence of obstacles). We examine the asymptotic behavior of the proposed metric in terms of the convergence to the geodesic distances in the underlying manifold of the domain, and provide stability analysis results for the topological persistence. The proposed framework and its convergences and stability analysis are demonstrated through numerical simulations and experiments.