scholarly journals Persistent Homology of Geospatial Data: A Case Study with Voting

SIAM Review ◽  
2021 ◽  
Vol 63 (1) ◽  
pp. 67-99
Author(s):  
Michelle Feng ◽  
Mason A. Porter
Keyword(s):  
Persistent Homology ◽  
Geospatial Data

scholarly journals Persistent Homology of Geospatial Data: A Case Study with Voting

2019 ◽  
Author(s):  
Michelle Feng ◽  
Mason A. Porter
Keyword(s):  
Simplicial Complex ◽  
Persistent Homology ◽  
Point Clouds ◽  
Simplicial Complexes ◽  
Geospatial Data ◽  
Alternative Methods ◽  
Data Set ◽  
New Methods ◽  
Complex Construction

A crucial step in the analysis of persistent homology is the transformation of data into an appropriate topological object (in our case, a simplicial complex). Modern packages for persistent homology often construct Vietoris–Rips or other distance-based simplicial complexes on point clouds because they are relatively easy to compute. We investigate alternative methods of constructing these complexes and the effects of making associated choices during simplicial-complex construction on the output of persistent-homology algorithms. We present two new methods for constructing simplicial complexes from two-dimensional geospatial data (such as maps). We apply these methods to a California precinct-level voting data set, demonstrating that our new constructions can capture geometric characteristics that are missed by distance-based constructions. Our new constructions can thus yield more interpretable persistence modules and barcodes for geospatial data. In particular, they are able to distinguish short-persistence features that occur only for a narrow range of distance scales (e.g., voting behaviors in densely populated cities) from short-persistence noise by incorporating information about other spatial relationships between precincts.

scholarly journals Evaluating Landscape Attractiveness with Geospatial Data, a Case Study in Flanders, Belgium

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 703
Author(s):  
Astrid Vannoppen ◽  
Jeroen Degerickx ◽  
Anne Gobin
Keyword(s):  
Ecosystem Services ◽  
Urban Areas ◽  
Policy Development ◽  
Bare Soil ◽  
Geospatial Data ◽  
Soil Conditions ◽  
Landscape Indicators ◽  
Landscape Characteristics ◽  
Landscape Quality

Attractive landscapes are diverse and resilient landscapes that provide a multitude of essential ecosystem services. The development of landscape policy to protect and improve landscape attractiveness, thereby ensuring the provision of ecosystem services, is ideally adapted to region specific landscape characteristics. In addition, trends in landscape attractiveness may be linked to certain policies, or the absence of policies over time. A spatial and temporal evaluation of landscape attractiveness is thus desirable for landscape policy development. In this paper, landscape attractiveness was spatially evaluated for Flanders (Belgium) using landscape indicators derived from geospatial data as a case study. Large local differences in landscape quality in (i) rural versus urban areas and (ii) between the seven agricultural regions in Flanders were found. This observed spatial variability in landscape attractiveness demonstrated that a localized approach, considering the geophysical characteristics of each individual region, would be required in the development of landscape policy to improve landscape quality in Flanders. Some trends in landscape attractiveness were related to agriculture in Flanders, e.g., a slight decrease in total agricultural area, decrease in dominance of grassland, maize and cereals, a decrease in crop diversity, sharp increase in the adoption of agri-environmental agreements (AEA) and a decrease in bare soil conditions in winter. The observed trends and spatial variation in landscape attractiveness can be used as a tool to support policy analysis, assess the potential effects of future policy plans, identify policy gaps and evaluate past landscape policy.

A Framework of Local Geospatial Data Infrastructure for Sustainable Urban Development

2014 ◽  
Vol 71 (4) ◽  
Author(s):  
Azman Ariffin ◽  
Nabila Ibrahim ◽  
Ghazali Desa ◽  
Uznir Ujang ◽  
Hishamuddin Mohd Ali ◽  
...  
Keyword(s):  
Urban Development ◽  
Spatial Data ◽  
Geospatial Data ◽  
Sustainable Urban Development ◽  
Data Infrastructure ◽  
Shape Category ◽  
Key Dimensions ◽  
Data Layers ◽  
Conceptual Data

This paper addresses the need to develop a Local Geospatial Data Infrastructure (LGDI) for sustainable urban development. This research will highlight the effective and efficient framework for the development of local infrastructure. This paper presents a framework (a combination of domain based and goal based frameworks) for developing a Local Geospatial Data Infrastructure. The basis of this research is on a case study conducted in a Malaysian city. The main focus of the case study was on measuring and assessing sustainability. Six conceptual frameworks were produced based on 6 key dimensions of sustainability. The developed framework consists of 6 conceptual data models and 6 conceptual data structures. It was concluded that 30 spatial data layers are needed of which 12 data layers are categorized as point shape, 17 data layers are categorized as polygon shape and 1 data layer as line shape category.

scholarly journals Comparison of Approaches for Urban Functional Zones Classification Based on Multi-Source Geospatial Data: A Case Study in Yuzhong District, Chongqing, China

2019 ◽  
Vol 11 (3) ◽  
pp. 660 ◽  
Author(s):  
Kai Cao ◽  
Hui Guo ◽  
Ye Zhang
Keyword(s):  
Rapid Development ◽  
Geospatial Data ◽  
Classification Model ◽  
Gradient Boosting ◽  
Support Vector ◽  
Night Time ◽  
Heat Map ◽  
Extreme Gradient Boosting ◽  
Functional Zones

Accurate and timely classification and monitoring of urban functional zones prove to be significant in rapidly developing cities, to better understand the real and varying urban functions of cities to support urban planning and management. Many efforts have been undertaken to identify urban functional zones using various classification approaches and multi-source geospatial datasets. The complexity of this category of classification poses tremendous challenges to these studies especially in terms of classification accuracy, but on the opposite, the rapid development of machine learning technologies provides us with new opportunities. In this study, a set of commonly used urban functional zones classification approaches, including Multinomial Logistic Regression, K-Nearest Neighbors, Decision Tree, Support Vector Machine (SVM), and Random Forest, are examined and compared with the newly developed eXtreme Gradient Boosting (XGBoost) model, using the case study of Yuzhong District, Chongqing, China. The investigation is based on multi-variate geospatial data, including night-time imagery, geotagged Weibo data, points of interest (POI) from Gaode, and Baidu Heat Map. This study is the first endeavor of implementing the XGBoost model in the field of urban functional zones classification. The results suggest that the XGBoost classification model performed the best and was able to achieve an accuracy of 88.05%, which is significantly higher than the other commonly used approaches. In addition, the integration of night-time imagery, geotagged Weibo data, POI from Gaode, and Baidu Heat Map has also demonstrated their values for the classification of urban functional zones in this case study.

scholarly journals A Socio-Ecological Approach to Mitigating Wildfire Vulnerability in the Wildland Urban Interface: A Case Study from the 2017 Thomas Fire

Fire ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 9 ◽  
Author(s):  
Crystal A. Kolden ◽  
Carol Henson
Keyword(s):  
Case Report ◽  
Community Partnerships ◽  
Mitigation Strategies ◽  
Geospatial Data ◽  
Ecological Approach ◽  
Extreme Wind ◽  
The Many ◽  
Extreme Fire ◽  
Vulnerability Mitigation

Wildfire disasters are one of the many consequences of increasing wildfire activities globally, and much effort has been made to identify strategies and actions for reducing human vulnerability to wildfire. While many individual homeowners and communities have enacted such strategies, the number subjected to a subsequent wildfire is considerably lower. Furthermore, there has been limited documentation on how mitigation strategies impact wildfire outcomes across the socio-ecological spectrum. Here we present a case report documenting wildfire vulnerability mitigation strategies undertaken by the community of Montecito, California, and how such strategies addressed exposure, sensitivity, and adaptive capacity. We utilize geospatial data, recorded interviews, and program documentation to synthesize how those strategies subsequently impacted the advance of the 2017 Thomas Fire on the community of Montecito under extreme fire danger conditions. Despite the extreme wind conditions and interviewee estimates of potentially hundreds of homes being consumed, only seven primary residences were destroyed by the Thomas Fire, and firefighters indicated that pre-fire mitigation activities played a clear, central role in the outcomes observed. This supports prior findings that community partnerships between agencies and citizens are critical for identifying and implementing place-based solutions to reducing wildfire vulnerability.

scholarly journals Comparative analysis of QGIS plugins for Web Maps creation

La Granja ◽  
2021 ◽  
Vol 34 (2) ◽  
pp. 8-26
Author(s):  
Lia Duarte ◽  
Catarina Queirós ◽  
Ana Cláudia Teodoro
Keyword(s):  
Information System ◽  
Comparative Analysis ◽  
Geographic Information System ◽  
Open Source ◽  
Open Source Software ◽  
Geographic Information ◽  
Geospatial Data ◽  
Advantages And Disadvantages ◽  
Gis Software

QGIS is a free and open-source software that allows viewing, editing, and analyzing georeferenced data. It is a Geographic Information System (GIS) software composed by tools that allow to manipulate geographic information and consequently to create maps which help to get a better understanding and organization of geospatial data. Unfortunately, maps created directly in the GIS desktop software are not automatically transferred to a website. This research aimed to compare publishing capabilities in different QGIS plugins to create Web Maps. This study analyzes four QGIS plugins (QGIS2Web, QGIS Cloud, GIS Cloud Publisher and Mappia Publisher), performing a comparison between them, considering their advantages and disadvantages, the free and subscription plans, the tools offered by each plugin and other generic aspects. The four plugins were tested in a specific case study to automatically obtain different Web Maps. This study could help users to choose the most adequate tools to publish Web Maps under QGIS software.

scholarly journals Towards Progressive Geospatial Information Processing on Web Systems: A Case Study for Watershed Analysis in Iowa

2021 ◽  
Author(s):  
Muneeb Shahid ◽  
Yusuf Sermet ◽  
Ibrahim Demir
Keyword(s):  
Data Processing ◽  
Web Application ◽  
Large Scale ◽  
Flow Direction ◽  
Geospatial Data ◽  
The Internet ◽  
Raster Data ◽  
Watershed Analysis ◽  
Client Side

Geographic Information Systems (GIS) are available as stand-alone desktop applications as well as web platforms for vector- and raster-based geospatial data processing and visualization. While each approach offers certain advantages, limitations exist that motivate the development of hybrid systems that will increase the productivity of users for performing interactive data analytics using multidimensional gridded data. Web-based applications are platform-independent, however, require the internet to communicate with servers for data management and processing which raises issues for performance, data integrity, handling, and transfer of massive multidimensional raster data. On the other hand, stand-alone desktop applications can usually function without relying on the internet, however, they are platform-dependent, making distribution and maintenance of these systems difficult. This paper presents RasterJS, a hybrid client-side web library for geospatial data processing that is built on the Progressive Web Application (PWA) architecture to operate seamlessly in both Online and Offline modes. A packaged version of this system is also presented with the help of Web Bundles API for offline access and distribution. RasterJS entails the use of latest web technologies that are supported by modern web browsers, including Service Workers API, Cache API, IndexedDB API, Notifications API, Push API, and Web Workers API, in order to bring geospatial analytics capabilities to large-scale raster data for client-side processing. Each of these technologies acts as a component in the RasterJS to collectively provide a similar experience to users in both Online and Offline modes in terms of performing geospatial analysis activities such as flow direction calculation with hydro-conditioning, raindrop flow tracking, and watershed delineation. A large-scale case study is included in the study for watershed analysis to demonstrate the capabilities and limitations of the library. The framework further presents the potential to be utilized for other use cases that rely on raster processing, including land use, agriculture, soil erosion, transportation, and population studies.

scholarly journals Semantically-Enriched Search Engine for Geoportals: A Case Study with ArcGIS Online

2020 ◽  
Vol 1 ◽  
pp. 1-17
Author(s):  
Gengchen Mai ◽  
Krzysztof Janowicz ◽  
Sathya Prasad ◽  
Meilin Shi ◽  
Ling Cai ◽  
...  
Keyword(s):  
Search Engine ◽  
Query Expansion ◽  
Scoring Function ◽  
Geospatial Data ◽  
Document Similarity ◽  
Geographic Information Retrieval ◽  
Limited Ability ◽  
Semantic Query ◽  
Implicit Information

Abstract. Many geoportals such as ArcGIS Online are established with the goal of improving geospatial data reusability and achieving intelligent knowledge discovery. However, according to previous research, most of the existing geoportals adopt Lucene-based techniques to achieve their core search functionality, which has a limited ability to capture the user’s search intentions. To better understand a user’s search intention, query expansion can be used to enrich the user’s query by adding semantically similar terms. In the context of geoportals and geographic information retrieval, we advocate the idea of semantically enriching a user’s query from both geospatial and thematic perspectives. In the geospatial aspect, we propose to enrich a query by using both place partonomy and distance decay. In terms of the thematic aspect, concept expansion and embedding-based document similarity are used to infer the implicit information hidden in a user’s query. This semantic query expansion framework is implemented as a semantically-enriched search engine using ArcGIS Online as a case study. A benchmark dataset is constructed to evaluate the proposed framework. Our evaluation results show that the proposed semantic query expansion framework is very effective in capturing a user’s search intention and significantly outperforms a well-established baseline – Lucene’s practical scoring function – with more than 3.0 increments in DCG@K (K=3,5,10).

Digital Elevation Modeling Analysis for Investigation of Gravity Hill Phenomena

2018 ◽  
Vol 7 (3) ◽  
pp. 25-38
Author(s):  
Rehan Jamil
Keyword(s):  
Magnetic Force ◽  
Geospatial Data ◽  
Kriging Method ◽  
The Road ◽  
Modeling Analysis ◽  
Digital Elevation ◽  
Significant Difference ◽  
Average Slope ◽  
Engine Power

This article presents an investigation of the gravity hill phenomena by analysing its geospatial data. Wadi Al Baida located in Al Madinah, Saudi Arabia has been taken as a case study. Wadi Al Baida is listed as one of the sites in the world where the said phenomena exists, but the site has been famous for any sort of magnetic force because the vehicles move uphill and on flat road without any engine power on. The topography of the valley is studied by using the geospatial data of the area and by creating Digital Elevation Models (DEMs) based on Point Kriging method of gridding. The results show that the area has natural slope and there is a significant difference in the elevations of the start and the end point of the valley. A longitudinal profile is also generated to validate the results obtained by DEMs and it is found that the road has an average slope of 2.6% which makes the vehicles and rolling objects move on their own due to the force of gravity. Also, the concept of gravity hill has been explained with the help of a diagram which is easy to understand by the readers.

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