For a quick demonstration of geom_sf(), I'm using albersusa to access the laea projected boundaries of the United States as a simple features (sf) data structure, but sf also makes it easy to read various file formats and even convert various spatial objects to sf. There are also a bunch of other R packages that, like albersusa, make it easy to query geo-spatial data as an sf data. The "Reverse dependencies" section of sf's CRAN page is a good place to discover them, but just to name a few: tidycensus, rnaturalearth, and mapsapi. One awesome consequence of using sf is that, since the data structure contains all the geo-spatial information, both plot() and geom_sf() just workTM. The most brilliant thing about sf is that it stores geo-spatial structures in a special list-column of a data frame.

Work with me or attend my 2 day workshop! Here's a quick example of reading a shape file into R as simple features via st_read(), then plotting those features (in this case, North Carolina counties) using each one of the four mapping approaches plotly provides. You might be wondering, "What can plotly offer over other interactive mapping packages such as leaflet, mapview, mapedit, etc?". One big feature is the linked brushing framework, which works best when linking plotly together with other plotly graphs (i.e., only a subset of brushing features are supported when linking to other crosstalk-compatible htmlwidgets). Another is the ability to leverage the plotly.js

Our lives today, and in the future, will necessarily pivot around the digitization of objects in the universe, through the efficient land, sea, and aerial surveys. The data collected will embed locational intelligence that will help us create maps with enhanced and meaningful spatial properties. These maps will form the substrate upon which the DNA of physical objects and their thematic properties will be seamlessly interwoven. The resulting rich datasets will become amenable to real-time analysis through Cloud computing that can be shared anytime, anywhere! Temporal resolution of the data is going to be crucial for real-time and near-real-time applications and thus controlled crowdsourcing with automated validation tools is bound to lead to more opportunities.

In this paper, we introduce a framework and our ongoing experiments in which computers learn to enact complex temporal-spatial actions by observing humans. Our framework processes motion capture data of human subjects performing actions, and uses qualitative spatial reasoning to learn multi-level representations for these actions. Using reinforcement learning, these observed sequences are used to guide a simulated agent to perform novel actions. To evaluate, we visualize the action being performed in an embodied 3D simulation environment, which allows evaluators to judge whether the system has successfully learned the novel concepts. This approach complements other planning approaches in robotics and demonstrates a method of teaching a robotic or virtual agent to understand predicate-level distinctions in novel concepts.

Traditionally it had been a problem that researchers did not have access to enough spatial data to answer pressing research questions or build compelling visualizations. Today, however, the problem is often that we have too much data. Spatially redundant or approximately redundant points may refer to a single feature (plus noise) rather than many distinct spatial features. We can use density-based clustering to compress such spatial data into a set of representative features. This paper demonstrates how to reduce the size of a spatial data set of GPS latitude-longitude coordinates using the Python programming language and its scikitlearn implementation of the DBSCAN density-based clustering algorithm. DBSCAN works very well in low-dimension space, such as the two-dimensional feature space in this geospatial example.

Our study aims to build a machine learning model for crime prediction using geospatial features for different categories of crime. The reverse geocoding technique is applied to retrieve open street map (OSM) spatial data. This study also proposes finding hotpoints extracted from crime hotspots area found by Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). A spatial distance feature is then computed based on the position of different hotpoints for various types of crime and this value is used as a feature for classifiers. We test the engineered features in crime data from Royal Canadian Mounted Police of Halifax, NS. We observed a significant performance improvement in crime prediction using the new generated spatial features.

Today I'm writing this post to explain how it's possible to make geographic analysis and answer questions like: which is the richest area in my city? How many people do live in one neighborhood? You can do it combining shape files with an excel spreadsheet, let's understand it together... Then, we're gonna need one shape file and one excel spreadsheet. I'm from Brazil, and we do have a lot of open data from States and Cities.

This paper studies a challenging problem of tracking severely occluded objects in long video sequences. The proposed method reasons about the containment relations and human actions, thus infers and recovers occluded objects identities while contained or blocked by others. There are two conditions that lead to incomplete trajectories: i) Contained. The occlusion is caused by a containment relation formed between two objects, e.g., an unobserved laptop inside a backpack forms containment relation between the laptop and the backpack. ii) Blocked. The occlusion is caused by other objects blocking the view from certain locations, during which the containment relation does not change. By explicitly distinguishing these two causes of occlusions, the proposed algorithm formulates tracking problem as a network flow representation encoding containment relations and their changes. By assuming all the occlusions are not spontaneously happened but only triggered by human actions, an MAP inference is applied to jointly interpret the trajectory of an object by detection in space and human actions in time. To quantitatively evaluate our algorithm, we collect a new occluded object dataset captured by Kinect sensor, including a set of RGB-D videos and human skeletons with multiple actors, various objects, and different changes of containment relations. In the experiments, we show that the proposed method demonstrates better performance on tracking occluded objects compared with baseline methods.

Spatial understanding is a fundamental problem with wide-reaching real-world applications. The representation of spatial knowledge is often modeled with spatial templates, i.e., regions of acceptability of two objects under an explicit spatial relationship (e.g., "on," "below," etc.). In contrast with prior work that restricts spatial templates to explicit spatial prepositions (e.g., "glass on table"), here we extend this concept to implicit spatial language, i.e., those relationships (generally actions) for which the spatial arrangement of the objects is only implicitly implied (e.g., "man riding horse"). In contrast with explicit relationships, predicting spatial arrangements from implicit spatial language requires significant common sense spatial understanding. Here, we introduce the task of predicting spatial templates for two objects under a relationship, which can be seen as a spatial question-answering task with a (2D) continuous output ("where is the man w.r.t. a horse when the man is walking the horse?"). We present two simple neural-based models that leverage annotated images and structured text to learn this task. The good performance of these models reveals that spatial locations are to a large extent predictable from implicit spatial language. Crucially, the models attain similar performance in a challenging generalized setting, where the object-relation-object combinations (e.g., "man walking dog") have never been seen before. Next, we go one step further by presenting the models with unseen objects (e.g., "dog"). In this scenario, we show that leveraging word embeddings enables the models to output accurate spatial predictions, proving that the models acquire solid common sense spatial knowledge allowing for such generalization.

According to (Zhang et Though compressive community sensing can effectively al. 2014a), there are two major roles in community sensing reduce the required incentives and participants, it still aggregates - the organizer and the participants - where the former is the real-time location and sensor data from each the individual or organization that creates the sensing task, participant, so as to first identify the covered subareas, fill recruits participants and collects the sensor data, while the with collected data, and then recover the missing data for latter (i.e., participants) involve in the sensing task and provide the rest. To protect the location privacy of participants, the the sensing data. Frequently, the organizer pursues a same of group of researchers (Wang et al. 2017a; 2016b) proposed high (or even full) spatial-temporal coverage of the collected to leverage the Differential Geo-Obfuscation to replace sensor data. However incentives (e.g., monetary rewards) and each participants' real-time location with a "mock" location the threats to privacy (e.g., exposing real-time locations) are while insuring the recovery accuracy. With the Differential two major concerns that may affect the willingness of the Geo-Obfuscation, the participants' locations are expected to participants to join a community sensing task.