We address the coarse-grained disambiguation of the spatial prepositions as the first step towards spatial role labeling using deep learning models. We propose a hybrid feature of word embeddings and linguistic features, and compare its performance against a set of linguistic features, pre-trained word embeddings, and corpus-trained embeddings using seven classical machine learning classifiers and two deep learning models. We also compile a dataset of 43,129 sample sentences from Pattern Dictionary of English Prepositions (PDEP). The comprehensive experimental results suggest that the combination of the hybrid feature and a convolutional neural network outperforms state-of-the-art methods and reaches the accuracy of 94.21% and F1-score of 0.9398.

Geospatial data is at the core of the Semantic Web, of which the largest knowledge base contains more than 30 billions facts. Reasoning on these large amounts of geospatial data requires efficient methods for the computation of links between the resources contained in these knowledge bases. In this paper, we present Radon – efficient solution for the discovery of topological relations between geospatial resources according to the DE9-IM standard. Our evaluation shows that we outperform the state of the art significantly and by several orders of magnitude.

Fine-grained image classification is challenging due to the large intra-class variance and small inter-class variance, aiming at recognizing hundreds of sub-categories belonging to the same basic-level category. Since two different sub-categories is distinguished only by the subtle differences in some specific parts, semantic part localization is crucial for fine-grained image classification. Most previous works improve the accuracy by looking for the semantic parts, but rely heavily upon the use of the object or part annotations of images whose labeling are costly. Recently, some researchers begin to focus on recognizing sub-categories via weakly supervised part detection instead of using the expensive annotations. However, these works ignore the spatial relationship between the object and its parts as well as the interaction of the parts, both of them are helpful to promote part selection. Therefore, this paper proposes a weakly supervised part selection method with spatial constraints for fine-grained image classification, which is free of using any bounding box or part annotations. We first learn a whole-object detector automatically to localize the object through jointly using saliency extraction and co-segmentation. Then two spatial constraints are proposed to select the distinguished parts. The first spatial constraint, called box constraint, defines the relationship between the object and its parts, and aims to ensure that the selected parts are definitely located in the object region, and have the largest overlap with the object region. The second spatial constraint, called parts constraint, defines the relationship of the object's parts, is to reduce the parts' overlap with each other to avoid the information redundancy and ensure the selected parts are the most distinguishing parts from other categories. Combining two spatial constraints promotes parts selection significantly as well as achieves a notable improvement on fine-grained image classification. Experimental results on CUB-200-2011 dataset demonstrate the superiority of our method even compared with those methods using expensive annotations.

It's easy to forget that even with the fanciest of machine learning models, we still need humans in the trenches cleaning input data. Descartes Labs, a startup that combines satellite imagery with data about our planet to produce insights and forecasts, knows this all too well. The company ended up building its own cloud-based parallel computing infrastructure to clean and process its massive corpus of satellite imagery. Companies like Descartes Labs cannot just throw raw satellite imagery into machine learning models to extract insights. Images captured contain clouds, cloud shadows and other atmospheric aberrations that make it impossible to compare images taken at different times.

Using analogical model formulation with sketches to solve Bennett Mechanical Comprehension Test problems.

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.

Everyone knows businesses today are grappling with the explosion of data. What's less well-known is the value of location data and the intelligence that geospatial analysis can provide business decision makers, beyond those that are GIS (geographic information system) professionals. Location data is already and will continue to be a growing component of all business data. Smartphone penetration is on the rise around the world, location infrastructure--such as cell towers, beacons, RFID and GPS--is proliferating, and the Internet of Things is poised to go mainstream within a few years. However, rather than adding to the complexity of the data landscape, location has the power to bring order to it.

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 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.