Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

Spatial Global Sensitivity Analysis of High Resolution classified topographic data use in 2D urban flood modelling

Since spatial transcriptomics can locate and distinguish the gene expression of functional genes in special regions and tissue, it is important for us to investigate the brain development, the development mechanism of brain diseases, and the relationship between brain structure and function in Neuroscience (or Brain science). While previous studies have introduced the crucial spatial transcriptomic techniques and data analysis methods, there are few studies to comprehensively overview the key methods, data resources, and technological applications of spatial transcriptomics in Neuroscience. For these reasons, we first investigate several common spatial transcriptomic data analysis approaches and data resources. Second, we introduce the applications of the spatial transcriptomic data analysis approaches in Neuroscience. Third, we summarize the integrating spatial transcriptomics with other technologies in Neuroscience. Finally, we discuss the challenges and future research directions of spatial transcriptomics in Neuroscience.

One function of the Community Land Model (CLM4) is the determination of surface albedo in the Community Earth System Model (CESM1). Because the typical spatial scales of CESM1 simulations are large compared to the scales of variability of surface properties such as snow cover and vegetation, unresolved surface heterogeneity is parameterized. Fractional snow‐covered area, or snow‐covered fraction (SCF), within a CLM4 grid cell is parameterized as a function of grid cell mean snow depth and snow density. This parameterization is based on an analysis of monthly averaged SCF and snow depth that showed a seasonal shift in the snow depth–SCF relationship. In this paper, we show that this shift is an artifact of the monthly sampling and that the current parameterization does not reflect the relationship observed between snow depth and SCF at the daily time scale. We demonstrate that the snow depth analysis used in the original study exhibits a bias toward early melt when compared to satellite‐observed SCF. This bias results in a tendency to overestimate SCF as a function of snow depth. Using a more consistent, higher spatial and temporal resolution snow depth analysis reveals a clear hysteresis between snow accumulation and melt seasons. Here, a new SCF parameterization based on snow water equivalent is developed to capture the observed seasonal snow depth–SCF evolution. The effects of the new SCF parameterization on the surface energy budget are described. In CLM4, surface energy fluxes are calculated assuming a uniform snow cover. To more realistically simulate environments having patchy snow cover, we modify the model by computing the surface fluxes separately for snow‐free and snow‐covered fractions of a grid cell. In this configuration, the form of the parameterized snow depth–SCF relationship is shown to greatly affect the surface energy budget. The direct exposure of the snow‐free surfaces to the atmosphere leads to greater heat loss from the ground during autumn and greater heat gain during spring. The net effect is to reduce annual mean soil temperatures by up to 3°C in snow‐affected regions.

GIS is a geo-referenced data based system for manipulating digital spatial and thematic data. Because of its unique properties of spatial data (spatial dependence and spatial heterogeneity), there is an urgent need to investigate the mechanisms of spatial processes through exploring spatial data. The techniques of spatial analysis can be categorized into six groups: (1) generation of spatial data and its preliminary treatment; (2) spatialization of attribute data and scale transformation of spatial data; (3) visualization and exploration of spatial data; (4) geo-statistics; (5) analysis of lattice data; and (6) reversing information from complicated spatial data. To meet the real world solutions, a procedural diagram of spatial data analysis is suggested as below: (1) preliminary data treatment; (2) modeling; (3) solution; and (4) interpretation. Various techniques are being employed to investigate spatial processes, the identical study objective and some common characteristics of the study object may lead to the integration and convergence of the various approaches, and further the introduction of a general science: spatial mathematics. A same important issue is to develop robust spatial analysis computer packages for public uses.

Recently, the necessity of new methods of spatial data integration and analysis in CRM has been increased since it is acknowledged that about eighty percent of all data stored in corporate databases has a spatial component. But conventional CRM systems are either incapable of managing spatial data or are not user-friendly when doing so. This paper has designed and implemented spatially-enabled integration management system that can manage consistently both enterprise and spatial data through a legacy CRM system and object-oriented database and additionally support spatial analysis and map visualization for a gCRM. Through implementation, it is demonstrated that the proposed system can facilitate effectively spatial data management and analysis in a legacy CRM system.

Event Abstract Back to Event Effective capacity-building for spatial analyses: Risk mapping for emerging infectious diseases in Indonesia Caitlin N. Pfeiffer1*, Mark A. Stevenson1, Farida Zenal2 and Luuk Schoonman2 1 Asia Pacific Centre for Animal Health, Faculty of Veterinary Science, University of Melbourne, Australia 2 Food and Agriculture Organization of the United Nations (Indonesia), Indonesia Emerging infectious diseases are an important threat to human and animal health worldwide, and many of these diseases are zoonoses. To understand the likelihood of disease emergence, spatial analyses are extremely valuable to facilitate targeting surveillance and risk mitigation activities to high-risk geographic areas. However, collecting and collating relevant existing data to facilitate spatial analyses can be challenging. Furthermore, relevant expertise in the spatial analytical methods may not be present in the countries at greatest risk for infectious disease emergence. Indonesia is known to be at increased risk for emerging vector-borne diseases and spill-over of diseases from wildlife into human populations1. The aim of this analytical and capacity-building project was to better understand the spatial variation in emerging infectious disease risk across the country, through upskilling of in-country staff to identify, gain access to, manage and analyse relevant existing data. Relevant spatial risk data included human demographic and health information, animal population distribution and health status, historical disease outbreak locations, and known disease drivers such as environmental data including temperature, rainfall, elevation, land use change over time and greenness indices. The project will produce risk maps that can be used to effectively communicate disease emergence risk with decision-makers, to facilitate policy decisions that can safeguard human, animal and environmental health. To facilitate the in-country analysis and interpretation of the data, a series of capacity building training activities were conducted in 2019. Five workshops were delivered in Indonesia to a team of trainees comprised of Government of Indonesia employees, academics from Indonesian universities and FAO Indonesia staff. The training courses focussed on the development of technical skills, providing practical exposure to analysis of spatial data, use of advanced software including R and WinBUGS, and the principles of risk mapping. Crucially, the delivery of this material as a series of workshops staggered over a three-month period allowed participants to build their skills and confidence in the relevant techniques over time. An important feature of this capacity-building activity was engagement of staff from multiple ministries of the Government of Indonesia (a One Health approach). This provided several important benefits, including insights about the nature and quality of existing data and knowledge of local factors that could potentially influence the spread of emerging infectious diseases. Additionally, the continued attendance of the same group of trainees facilitated the development of collaborative working relationships between staff from different Government of Indonesia ministries, providing a rich foundation for future collaborative and One Health work. Relevant local examples were used throughout the training activities which enhanced the trainees’ engagement with the technically challenging content. Evaluation at the end of each workshop indicated progressive skill development and increasing theoretical understanding throughout the series of courses. This series of workshops serves as a model for empowering more countries to deliver technically advanced analyses, to better protect human and animal health. The One Health approach was very beneficial for these spatial analyses. Expertise from a variety of disciplines contributed to a thorough understanding of each dataset and facilitated the selection of appropriate techniques. As sophisticated spatial analyses become increasingly feasible (through the collection and availability of data and increasing availability of software), it is important to consider how capacity to perform these analyses can be fostered in countries with previously limited technical expertise. Expert input is needed to determine which analytical techniques might provide answers to important research and policy questions. Expertise in training and adult education is also needed, to support technical staff in developing the knowledge and skills needed to competently perform the analyses. Substantial focus on the policy relevance, effective visualisation and communication of analyses, rather than just their technical features, will be required. The delivery of effective capacity-building activities, such as the example described in this work, will help ensure spatial analyses can be meaningfully implemented in the parts of our world where they are most needed. Acknowledgements This project is part of the Emerging Pandemic Threats Phase 2 (EPT-2) programme, funded by the U.S. Agency for International Development (USAID). We thank FAO Indonesia for their support of the program and all of the participating trainees for their dedication to the workshop material.

Big data and natural disasters: New approaches for spatial and temporal massive data analysis

Computational science has significantly advanced theory and experiment over many decades. Specifically, computational geography emerged in the 1980s in response to the reductionist limitations of early GIS software, which hindered deeper analyses of complex geographic data. The advent of relational databases further facilitated the use of computational methods in spatial data analysis. This study discusses the application of computational techniques and geocomputation in the spatial analysis of geographical phenomena. A literature search and data synthesis were conducted, followed by an exploration of computational methods, geocomputation, spatial data representation, storage and organization, spatial analytics, and GeoAI. Geocomputation is defined as the application of computer- intensive approaches, particularly those employing non-conventional data clustering and analysis methods, to discern knowledge. At present, these computational techniques enable the integration of diverse fields, supporting spatial analyses that require resources or ontological frameworks beyond the capabilities of traditional GIS software.

Geographic Artificial Intelligence (GeoAI) and Natural Language Processing (NLP) are two rapidly advancing technologies in spatial data analysis. GeoAI integrates artificial intelligence with geographic data to identify patterns, make predictions, and support decision-making. Meanwhile, NLP enables the processing and analysis of textual data related to spatial information, such as documents, reports, or geographic descriptions. The objective of this research is to obtain a representation of spatial data patterns through a series of processes, including problem identification, needs analysis, data collection and processing, document representation, and the application of Geographic Artificial Intelligence (GeoAI), Natural Language Processing (NLP), and Fuzzy Similarity methods to spatial or textual tax data and textual land data to identify data similarities. The research explores the integration of GeoAI and NLP in spatial data analysis to enhance the efficiency and accuracy of geographic data interpretation. The methods used in this study are based on artificial intelligence, which extracts spatial information from text and performs machine learning-based spatial analysis. The results demonstrate that the combination of GeoAI and NLP can improve the understanding of spatial patterns in unstructured data and support location-based decision-making processes. This research contributes to the development of more accurate spatial data analysis techniques that can be applied in various fields, such as urban planning, disaster management, and environmental analysis.

The relationship between spatial analysis and GIS has been a debating point for over a decade. Some see GIS as leading the way to a new era in which the desire and the ability to analyse spatial data is widespread, not just in geography but throughout all disciplines in which spatial data are encountered. Others see GIS as simply providing a medium for the recycling of out-dated spatial analytical techniques and models. This paper describes a role for GIS through the development of new forms of ‘local’ or ‘context-dependent’ spatial analytical methods in which the focus is on exceptions to the general trend represented by the more traditional ‘global’ methods.

: The principle objective of this project is to identify and implement methods for the analysis of error and uncertainty of spatial data in spatially explicit ecological models. The development of these methods for spatial error and uncertainty analysis is done in coordination with Dr. George Gertner and SERDP Conservation Thrust Area Project SC-1096. Methods and tools developped and implemented by our project will be transferred to Project CS-1096 for incorporation within that project's error budget framework and toolbox. Our software tools will also be compatible with the specifications and requirements of DoD and SERDP land management tools and software like the U.S. Army Corps of Engineers' Land Management System (LMS) or others. We have completed the first phase of a survey of existing methods for the analysis of error and uncertainty analysis in spatial data. From this survey we have formulated a general framework for spatial error and uncertainty analysis of ecological models, and we have implemented software tools for treatment of error and uncertainty in categorical spatial data. We have begun the process of testing and refining these software tools.

The dynamics of the Indonesian population has increased causing the need for cadastral objects to increase. 2D and 3D cadastral objects change over time. Cadastral objects have spatial and juridical data changes every day. These changes can be caused by the transfer of rights, changes of rights, split and merging cadastral objects. Changes are recorded and stored as a history of cadastral objects (4-dimensional cadastre). Historical data on cadastral objects can be categorized as one of the big data in the cadastre sector, based on the frequency of recording data. The data can be used to track changes in cadastral objects so that they can avoid disputes. Spatial data in this case is very vulnerable data to cause disputes if the shape, position and size do not match the conditions in the field, and need special attention in integrating with the juridical data. This paper aims to identify methods for storing spatial data of 4D cadastral objects that are suitable for Indonesia. The importance of storing cadastral objects and their history causes researchers and cadastre experts in the world to formulate international standards in managing spatial and juridical data along with a history of cadastral objects (4D cadastre). LADM is an international standard conceptual model (ISO 19152) which can show the legacy of a cadastral object expressed in RRR (Rights, Restriction and Responsibility). In LADM, there is a VersionedObject class that represents various versions or a history of spatial units, object registration, owner and administration. Several studies conducted by researchers in the world were compared in this paper to find methods for applying LADM and VersionedObject classes. That methods were implemented in this paper to design a database of spatio-temporal 4D cadastre. Finally a prototype of spatial-temporal database will be produced to manage 4D cadastral objects.

The extensive use of Information and Communication Technologies and the consequent unprecedented generation of data have radically transformed the way we understand cities today. The vision of smart cities considers technology as an enabling force for the emergence of new forms of intelligence and collaboration, enhancing, thus, the problem-solving capacity of the city. Despite the wide range of applications aiming to improve urban systems and city governance, urban planning processes are rarely informed by online platforms and data generated by them lack comprehensive data visualization approaches. This research introduces an exploratory approach to exploit urban data through interactive visualization and game design, as a way to facilitate the access and understanding of such data. A novel methodology is proposed, leveraging on spatial data as an input source which drives the generation of three-dimensional environments and interactive applications supported by game engines. More specifically, this research builds upon existing tools and methods for geoprocessing and spatial analysis and embeds them in a 3D environment that employs game design elements. Three indicative visualization scenarios are designed, developed, and implemented to showcase the dynamics and flexibility of the proposed methodology, based on the registry of an urban reporting application in Thessaloniki.

The origins of modern spatial analysis lie in the development of quantitative geography and regional science in the late 1950s. The use of quantitative procedures and techniques to analyse patterns of points, lines, areas and surfaces depicted on analogue maps or defined by co-ordinates in two-or three-dimensional space characterise the initial stage. Later on, more emphasis was placed on the indigenous features of geographical space, on spatial choices and processes, and their implications for the spatio-temporal evolution of complex spatial systems. Spatial analysis, as it has become over the past four decades, is more than spatial statistics and data analysis, and goes far beyond data sampling, data manipulation, exploratory and confirmatory spatial data analysis, into areas of spatial modelling encompassing a large and diverse set of models in both the environmental and the social sciences. In the environmental sciences, they range from physical dispersion models (e.g. for suspended particulates), chemical reaction models (e.g. photochemical smog), and biological systems models (e.g. for ecosystems in water), through deterministic process and stochastic process models of regional environmental quality management, and integrated models for environment-energy-economic assessment. For many models, especially the physical dispersion models, chemical reaction models, and biological systems models, the geographical location of the site is not considered to be of overriding importance. The variation in model results over space can be achieved by obtaining different inputs to the area in question. Process models include deterministic versions which attempt to describe a particular sequence in terms of known physical laws, and stochastic model versions which aim to describe a particular process such as erosion, groundwater movement and absorption of pollutants in terms of probability theory. For such processes, there is an interaction between the spatial process and a substrate. This substrate provides a one-, two-or three-dimensional framework within which the process model can operate. The space in which the process is modelled is disaggregated into a set of finite elements which are usually assumed to be internally homogeneous. Two-dimensional models generally use as their data small pixels (see Fischer et al., 1996).

Peer and neighborhood effects: Citation analysis using a spatial autoregressive model and pseudo-spatial data