ACM Transactions on Spatial Algorithms and Systems (TSAS) is a scholarly journal that publishes the highest quality papers on all aspects of spatial algorithms and systems and closely related disciplines. It has a multi-disciplinary perspective in that it spans a large number of areas where spatial data is manipulated or visualized (regardless of how it is specified - i.e., geometrically or textually) such as geography, geographic information systems (GIS), geospatial and spatiotemporal databases, spatial and metric indexing, location-based services, web-based spatial applications, geographic information retrieval (GIR), spatial reasoning and mining, security and privacy, as well as the related visual computing areas of computer graphics, computer vision, geometric modeling, and visualization where the spatial, geospatial, and spatiotemporal data is central.

ACM Transactions on Spatial Algorithms and Systems (TSAS) is a scholarly journal that publishes high-quality papers on all aspects of spatial algorithms and systems, as well as closely related disciplines. It adopts a multidisciplinary perspective, covering a wide range of areas where spatial data is manipulated or visualized. These areas include geography, geographic information systems (GIS), geospatial and spatiotemporal databases, spatial and metric indexing, location-based services, web-based spatial applications, geographic information retrieval (GIR), spatial reasoning and mining, security and privacy, along with related visual computing fields such as computer graphics, computer vision, geometric modeling, and visualization, where spatial, geospatial, and spatiotemporal data are central.

《ACM Transactions on Spatial Algorithms and Systems》是一本学术期刊，致力于发表关于空间算法和系统及相关学科的高质量论文。该期刊具有多学科的视角，涵盖了多个领域，其中空间数据的处理或可视化是核心内容。这些领域包括地理学、地理信息系统（GIS）、地理空间和时空数据库、空间和度量索引、基于位置的服务、基于网络的空间应用、地理信息检索（GIR）、空间推理与挖掘、安全与隐私，以及与计算机图形学、计算机视觉、几何建模和可视化等相关的视觉计算领域。

ACM Transactions on Spatial Algorithms and Systems

Thanks to the prevalence of mobile phones and GPS devices, spatiotemporal population data can be obtained easily. In this article, we propose a mixture of collective graphical models for estimating people flow from spatiotemporal population data. The spatiotemporal population data we use as input is the number of people in each grid cell area over time, which is aggregated information about many individuals; to preserve privacy, they do not contain trajectories of each individual. Therefore, it is impossible to directly estimate people flow. To overcome this problem, the proposed model assumes that transition populations are hidden variables and estimates the hidden transition populations and transition probabilities simultaneously. The proposed model can handle changes of people flow over time by segmenting time-of-day points into multiple clusters, where different clusters have different flow patterns. We develop an efficient variational Bayesian inference procedure for the collective graphical mixture model. In our experiments, the effectiveness of the proposed method is demonstrated by using four real-world spatiotemporal population datasets in Tokyo, Osaka, Nagoya, and Beijing.

Tomoharu Iwata

Hitoshi Shimizu

Futoshi Naya

Naonori Ueda

Estimating People Flow from Spatiotemporal Population Data via Collective Graphical Mixture Models

通过集体图形混合模型从时空人口数据中估计人流

Urban planning refers to the efforts of designing land-use configurations given a region. However, to obtain effective urban plans, urban experts have to spend much time and effort analyzing sophisticated planning constraints based on domain knowledge and personal experiences. To alleviate the heavy burden of them and produce consistent urban plans, we want to ask that can AI accelerate the urban planning process, so that human planners only adjust generated configurations for specific needs? The recent advance of deep generative models provides a possible answer, which inspires us to automate urban planning from an adversarial learning perspective. However, three major challenges arise: (1) how to define a quantitative land-use configuration? (2) how to automate configuration planning? (3) how to evaluate the quality of a generated configuration? In this article, we systematically address the three challenges. Specifically, (1) We define a land-use configuration as a longitude-latitude-channel tensor. (2) We formulate the automated urban planning problem into a task of deep generative learning. The objective is to generate a configuration tensor given the surrounding contexts of a target region. In particular, we first construct spatial graphs using geographic and human mobility data crawled from websites to learn graph representations. We then combine each target area and its surrounding context representations as a tuple, and categorize all tuples into positive (well-planned areas) and negative samples (poorly-planned areas). Next, we develop an adversarial learning framework, in which a generator takes the surrounding context representations as input to generate a land-use configuration, and a discriminator learns to distinguish between positive and negative samples. (3) We provide quantitative evaluation metrics and conduct extensive experiments to demonstrate the effectiveness of our framework.

Dongjie Wang

Yanjie Fu

Kunpeng Liu

Fanglan Chen

Pengyang Wang

Chang-Tien Lu

Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation

通过对抗学习重新构想城市布局的自动化城市规划：量化、生成与评估

Personalized location recommendation is an increasingly active topic in recent years, which recommends appropriate locations to users based on their temporal and geospatial visiting patterns. Current location recommendation methods usually estimate the users’ visiting preference probabilities from the historical check-ins in batch. However, in practice, when users’ behaviors are updated in real-time, it is often cost-inhibitive to re-estimate and updates users’ visiting preference using the same batch methods due to the number of check-ins. Moreover, an important nature of users’ movement patterns is that users are more attracted to an area where have dense locations with same categories for conducting specific behaviors. In this paper, we propose a location recommendation method called GeoRTGA by utilizing the real time user behaviors and geographical attractions to tackle the problems. GeoRTGA contains two sub-models: real time behavior recommendation model and attraction-based spatial model. The real time behavior recommendation model aims to recommend real-time possible behaviors which users prefer to visit, and the attraction-based spatial model is built to discover the category-based spatial and individualized spatial patterns based on the geographical information of locations and corresponding location categories and check-in numbers. Experiments are conducted on four public real-world check-in datasets, which show that the proposed GeoRTGA outperforms the five existing location recommendation methods.

Xinyu Ren

Seyyed Mohammadreza Rahimi

Xin Wang

Utilization of Real Time Behavior and Geographical Attraction for Location Recommendation

实时行为与地理吸引力在位置推荐中的应用

Floor identification has gained much attention due to the increasing demand for indoor location-based services, especially prompt emergency response services. Leveraging cellular signals for floor identification has recently been of interest due to the pervasiveness of cellular technology. However, all current systems require information from multiple cell towers concurrently, which is inaccessible in most phones and thus severely limits their deployability. We propose UniCellular , a ubiquitous and deployable floor identification system. UniCellular is the first system to meet the regulatory agencies’ accuracy requirements using received signal strength information from only the serving cell tower. UniCellular relies on a sequence of signal measurements to overcome the limited information available when using only the serving tower. Our system handles multiple challenges affecting accuracy and deployability, including noisy cellular data, overfitting, data collection overhead, and suitability for mobile device deployment. Moreover, UniCellular employs recent advances in deep generative models to improve the system’s robustness to unseen noisy data and reduce data collection overhead. Our extensive experiments verify UniCellular ’s effectiveness in multiple real testbeds, where it correctly estimates the user’s exact floor up to 98.7% of the time using only the serving tower, an improvement up to 310% compared to the state-of-the-art cellular-based system. Furthermore, UniCellular can remain regulatory-compliant with up to 70% reduction in available training data. UniCellular achieves these improvements with over an order of magnitude reduction in the model size compared to the state-of-the-art. This shows UniCellular ’s superior performance while providing a ubiquitous, efficient, and practical solution that meets regulatory requirements.

Sherif Mostafa

Moustafa Youssef

Khaled A. Harras

Ubiquitous and Low-Overhead Floor Identification with Limited Cellular Information

基于有限蜂窝信息的普适且低开销的楼层识别

Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among others. Deep learning methods are particularly promising for modeling many remote sensing tasks given the success of deep neural networks in similar computer vision tasks and the sheer volume of remotely sensed imagery available. However, the variance in data collection methods and handling of geospatial metadata make the application of deep learning methodology to remotely sensed data nontrivial. For example, satellite imagery often includes additional spectral bands beyond red, green, and blue and must be joined to other geospatial data sources that may have differing coordinate systems, bounds, and resolutions. To help realize the potential of deep learning for remote sensing applications, we introduce TorchGeo, a Python library for integrating geospatial data into the PyTorch deep learning ecosystem. TorchGeo provides data loaders for a variety of benchmark datasets, composable datasets for uncurated geospatial data sources, samplers for geospatial data, and transforms that work with multispectral imagery. TorchGeo is also the first library to provide pre-trained models for multispectral satellite imagery (e.g., models that use all bands from the Sentinel-2 satellites), allowing for advances in transfer learning on downstream remote sensing tasks with limited labeled data. We use TorchGeo to create reproducible benchmark results on existing datasets and benchmark our proposed method for preprocessing geospatial imagery on the fly. TorchGeo is open source and available on GitHub: https://github.com/microsoft/torchgeo.

Adam J. Stewart

Caleb Robinson

Isaac A. Corley

Anthony Ortiz

Juan M. Lavista Ferres

Arindam Banerjee

TorchGeo: Deep Learning With Geospatial Data

TorchGeo: 使用地理空间数据的深度学习

An important task of pattern recognition and map generalization is to partition a set of disjoint polygons into groups and to aggregate the polygons within each group to a representative output polygon. We introduce a new method for this task called bicriteria shapes. Following a classical approach, we define the output polygons by merging the input polygons with a set of triangles that we select from a conforming Delaunay triangulation of the input polygons’ exterior. The innovation is that we control the selection of triangles with a bicriteria optimization model that is efficiently solved via graph cuts. In particular, we minimize a weighted sum that combines the total area of the output polygons and their total perimeter. In a basic problem, we ask for a single solution that is optimal for a preset parameter value. In a second problem, we ask for a set containing an optimal solution for every possible value of the parameter. We discuss how this set can be approximated with few solutions and show that it is hierarchically nested. Hence, the output is a hierarchical clustering that corresponds to multiple levels of detail. An evaluation with building footprints as input and a comparison with α -shapes that are based on the same underlying triangulation conclude the article. An advantage of bicriteria shapes compared to α -shapes is that the sequence of solutions for decreasing values of the parameter is monotone with respect to the total perimeter of the output polygons, resulting in a monotonically decreasing visual complexity.

Peter Rottmann

Anne Driemel

Herman Haverkort

Heiko Röglin

Jan-Henrik Haunert

Bicriteria Shapes: Hierarchical Grouping and Aggregation of Polygons with an Efficient Graph-Cut Approach

双准则形状：采用高效图割方法的分层分组与多边形聚合

The popularity of smartphones and 4G/5G network have enabled various novel transportation modes based on shared mobility, such as app-based ride-hailing services (e.g., Uber and Lyft) and shared micromobility services (e.g., Veo and Gotcha). However, little is known about to what degree their operations impact urban traffic, which is important for transportation planning and policy making. These companies seldom share their ride data due to business and user privacy reasons, and they are young and still exploring and growing their market to new locations. Recently, transportation engineering researchers began to collect data in large cities trying to understand the transportation impacts of shared mobility services, but (1) there does not exist a general analytics framework applicable to any city, and (2) the studies were based on historical data and cannot project the future easily to catch up with the rapid development of shared mobility services. In this paper, we introduce a general framework for multi-modal urban traffic analytics. The goal is to build a digital twin of the transportation in a city, i.e., an accurate agent-based transportation simulation model, based on a medium-sized dataset of the interested transportation modes collected by the research group combined with other open data sources such as US Census Bureau. With this digital twin, transportation engineering researchers can flexibly analyze the impact of shared mobility services under different scenarios, such as “if the number of Uber drivers doubles” or “if the number of deployed e-scooters doubles”. The digital twin may also enable new opportunities, such as being an environment for learning policies with reinforcement learning. Our framework consists of three steps: (1) fitting the spatiotemporal distribution of the shared mobility rides on the underlying road network, (2) generating the travel day-plans of the entire urban population based on the learned spatiotemporal ride distribution and user-specified parameters, and (3) configuring an agent-based simulation software such as MATSim to execute the generated realistic day-plans, which provides detailed transportation data for analytics. At the core of our approach is a new spatiotemporal network kernel density estimation (KDE) method that fixes the flaw of prior methods where the contributions of different data samples are not equal. We also propose a crowdsourcing method to collect app-based ride data that is easy to carry out in any city. Using Birmingham, AL as an example, we demonstrate how our framework can be applied to help transportation engineering researchers analyze the impacts of Uber ride-hailing and Veo e-scooter services.

Jalal Khalil

Da Yan

Lyuheng Yuan

Mostafa Jafarzadehfadaki

Saugat Adhikari

Jiao Han

Virginia Sisiopiku

Zhe Jiang

Urban Traffic Simulation with Shared Mobility Services: An Approach Using Spatiotemporal Network Kernel Density Estimation and MATSim

城市交通模拟与共享出行服务：基于时空网络核密度估计和MATSim的方法

The accurate and prompt mapping of flood-affected regions is important for effective disaster management, including damage assessment and relief efforts. While high-resolution optical imagery from satellites during disasters presents an opportunity for automated flood inundation mapping, existing segmentation models face challenges due to noises like cloud cover and tree canopies. Thanks to the digital elevation model (DEM) data readily available from sources such as United States Geological Survey (USGS), terrain guidance was utilized by recent graphical models such as hidden Markov trees (HMTs) to improve segmentation quality. Unfortunately, these methods either can only handle a small area where water levels at different locations are assumed to be consistent, or require restricted assumptions such as there is only one river channel. This paper presents an algorithm for flood extent mapping on large-scale Earth imagery, applicable to a large geographic area with multiple river channels. Since water level can vary a lot from upstream to downstream, we propose to detect river pixels in order to partition the remaining pixels into localized zones, each with a unique water level. In each zone, water at all locations flow to the same river entry point. Pixels in each zone are organized by an HMT to capture water flow directions guided by elevations. Moreover, a novel regularization scheme is designed to enforce inter-zone consistency by penalizing pixel-pairs of adjacent zones that violate terrain guidance. Efficient parallelization is made possible by coloring the zone adjacency graph to identify zones and zone-pairs that have no dependency and hence can be processed in parallel, and incremental one-pass terrain-guided scanning is conducted wherever applicable to reuse computations. Experiments demonstrate that our solution is more accurate than existing solutions, and can efficiently and accurately map out flooding pixels in a giant area of size 24,805 × 40,129. Despite the imbalanced workloads caused by a few large zonal HMTs dominating the serial computing time, our parallelization approach is effective and manages to achieve up to 14.3 × speedup on a machine with Intel Xeon Gold 6126 CPU @ 2.60GHz (24 cores, 48 threads) using 32 threads.

Zelin Xu

Yupu Zhang

Arpan Sainju

Yang Zhou

Scaling Terrain-Aware Spatial Machine Learning for Flood Mapping on Large Scale Earth Imagery Data

大规模地球影像数据洪灾制图的地形感知空间机器学习扩展

Abbreviation	ACM Trans. Spatial Algorithms Syst.
Journal Impact	1.35
Quartiles(Global)	REMOTE SENSING(Q4)

ISSN	2374-0353, 2374-0361
h-index	18

Publication Information	Publisher: Association for Computing Machinery (ACM)，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2015，Proportion of original research papers: 100.00%，Self Citation Rate:8.30%， Gold OA Rate: 5.00%

ACM Transactions on Spatial Algorithms and Systems

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Estimating People Flow from Spatiotemporal Population Data via Collective Graphical Mixture Models

Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation

Utilization of Real Time Behavior and Geographical Attraction for Location Recommendation

Ubiquitous and Low-Overhead Floor Identification with Limited Cellular Information

TorchGeo: Deep Learning With Geospatial Data

Ubiquitous and Low-Overhead Floor Identification with Limited Cellular Information

TorchGeo: Deep Learning With Geospatial Data

Bicriteria Shapes: Hierarchical Grouping and Aggregation of Polygons with an Efficient Graph-Cut Approach

Urban Traffic Simulation with Shared Mobility Services: An Approach Using Spatiotemporal Network Kernel Density Estimation and MATSim

Scaling Terrain-Aware Spatial Machine Learning for Flood Mapping on Large Scale Earth Imagery Data