The document discusses the new spatial data capabilities in Microsoft SQL Server 2008 R2. It introduces the geography and geometry data types for storing geospatial and planar spatial data respectively. It describes how these data types allow spatial queries and analysis to be performed directly in the database. Integration with tools like Virtual Earth is also discussed, allowing location-based applications and visualizations to be built.
Spatial Data Concepts: Introduction to GIS,
Geographically referenced data, Geographic, projected
and planer coordinate system, Map projections, Plane
coordinate systems, Vector data model, Raster data
model
Data Input and Geometric transformation: Existing
GIS data, Metadata, Conversion of existing data,
Creating new data, Geometric transformation, RMS
error and its interpretation, Resampling of pixel
values.
Attribute data input and data display : Attribute data in
GIS, Relational model, Data entry, Manipulation of
fields and attribute data, cartographic symbolization,
types of maps, typography, map design, map
production
Data exploration: Exploration, attribute data query,
spatial data query, raster data query, geographic
visualization
Vector data analysis: Introduction, buffering, map
overlay, Distance measurement and map manipulation.
Raster data analysis: Data analysis environment, local
operations, neighbourhood operations, zonal
operations, Distance measure operations.
Spatial Interpolation: Elements, Global methods, local
methods, Kriging, Comparisons of different methods
Spatial Data Concepts: Introduction to GIS,
Geographically referenced data, Geographic, projected
and planer coordinate system, Map projections, Plane
coordinate systems, Vector data model, Raster data
model
Data Input and Geometric transformation: Existing
GIS data, Metadata, Conversion of existing data,
Creating new data, Geometric transformation, RMS
error and its interpretation, Resampling of pixel
values.
Attribute data input and data display : Attribute data in
GIS, Relational model, Data entry, Manipulation of
fields and attribute data, cartographic symbolization,
types of maps, typography, map design, map
production
Data exploration: Exploration, attribute data query,
spatial data query, raster data query, geographic
visualization
Vector data analysis: Introduction, buffering, map
overlay, Distance measurement and map manipulation.
Raster data analysis: Data analysis environment, local
operations, neighbourhood operations, zonal
operations, Distance measure operations.
Spatial Interpolation: Elements, Global methods, local
methods, Kriging, Comparisons of different methods
A GIS is a particular form of Information System applied to geographical data.
An Information System is a set of processes, executed on raw data to produce information which will be useful when making decisions.
GIS is not only a tool for making maps, it is a system for data analysis.
Digital Elevation Model (DEM) is the digital representation of the land surface elevation with respect to any reference datum. DEM is frequently used to refer to any digital representation of a topographic surface. DEM is the simplest form of digital representation of topography. GIS applications depend mainly on DEMs, today.
Data Entry and Preparation Spatial Data Input: Direct spatial data capture, Indirect spatial data captiure, Obtaining spatial data elsewhere Data Quality: Accuracy and Positioning, Positional accuracy, Attribute accuracy, Temporal accuracy, Lineage, Completeness, Logical consistency Data Preparation: Data checks and repairs, Combining data from multiple sources Point Data Transformation: Interpolating discrete data, Interpolating continuous data
A GIS is a particular form of Information System applied to geographical data.
An Information System is a set of processes, executed on raw data to produce information which will be useful when making decisions.
GIS is not only a tool for making maps, it is a system for data analysis.
Digital Elevation Model (DEM) is the digital representation of the land surface elevation with respect to any reference datum. DEM is frequently used to refer to any digital representation of a topographic surface. DEM is the simplest form of digital representation of topography. GIS applications depend mainly on DEMs, today.
Data Entry and Preparation Spatial Data Input: Direct spatial data capture, Indirect spatial data captiure, Obtaining spatial data elsewhere Data Quality: Accuracy and Positioning, Positional accuracy, Attribute accuracy, Temporal accuracy, Lineage, Completeness, Logical consistency Data Preparation: Data checks and repairs, Combining data from multiple sources Point Data Transformation: Interpolating discrete data, Interpolating continuous data
The economic competitiveness of Northeast Ohio relies upon a strong, diverse talent base to develop new innovations and successfully compete in an increasingly competitive world.
Nortech appoints Johnathan Holifield as the nation's first Vice President of Inclusive Competitiveness to help develop economic strategies that will include investments in the diverse landscape of Northeast Ohio's pools of talent and connect them to regional opportunities.
America21 co-founder, Johnathan Holifield, coined the term, "Inclusive Competitiveness," and now holds that position at Nortech as the tech-based economic engine commits to bridging the economic divide and ensuring the 21 counties it covers are a region of inclusive economic opportunity for all residents.
Concepts and Methods of Embedding Statistical Data into Maps IJSRP Journal
The main focus of this study is to find appropriate and stable solutions for representing the statistical data into map with some special features. This research also includes the comparison between different solutions for specific features. In this research I have found three solutions using three different technologies namely Oracle MapViewer, QGIS and AnyMap which are different solutions with different specialties. Each solution has its own specialty so we can choose any solution for representing the statistical data into maps depending on our criteria’s.
The main focus of this study is to find appropriate and stable solutions for representing the statistical data into map with some special features. This research also includes the comparison between different solutions for specific features. In this research I have found three solutions using three different technologies namely Oracle MapViewer, QGIS and AnyMap which are different solutions with different specialties. Each solution has its own specialty so we can choose any solution for representing the statistical data into maps depending on our criteria’s.
React’s suitability to develop Geospatial solutions.pdfMindfire LLC
Application development is a critical necessity for many firms that seek to create unique user experiences. This can range from a simple interface that provides relatively specialized access to data, such as data monetization, to a more comprehensive solution that supports complicated business decisions, such as site selection. According to 182 Pages Report, the worldwide geospatial solutions market is expected to reach USD 502.6 billion by 2024, up from USD 239.1 billion in 2019, at a CAGR of 13.2 percent over the forecast period. In this article, we will discuss the significance of react in geospatial solutions.
Presentation from 2009 LandmanXchange Conference in Dallas, TX. Provides the concept and need for GIS and GPS in Land Services, Land Management, and Surface or ROW management.
React’s suitability to develop Geospatial solutions.pdfMindfire LLC
Application development is a critical necessity for many firms that seek to create unique user experiences. This can range from a simple interface that provides relatively specialized access to data, such as data monetization, to a more comprehensive solution that supports complicated business decisions, such as site selection. According to 182 Pages Report, the worldwide geospatial solutions market is expected to reach USD 502.6 billion by 2024, up from USD 239.1 billion in 2019, at a CAGR of 13.2 percent over the forecast period. In this article, we will discuss the significance of react in geospatial solutions.
The main focus of this study is to find appropriate and stable solutions for representing the statistical data into map with some special features. This research also includes the comparison between different solutions for specific features. In this research I have found three solutions using three different technologies namely Oracle MapViewer, QGIS and AnyMap which are different solutions with different specialties. Each solution has its own specialty so we can choose any solution for representing the statistical data into maps depending on our criteria’s.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
Sql server 2008 r2 spatial data whitepaper
1. Delivering Location Intelligencewith Spatial Data<br />White Paper<br />Published: August 2007<br />Summary: The growing ability of businesses and consumers to quickly absorb large volumes of data, together with the increased availability of digital maps and spatially-enabled applications has created an unprecedented opportunity to incorporate geographic factors into decision making processes and analysis. The new spatial support in Microsoft SQL Server™ 2008 R2 can help you to make better decisions through visual analysis of location data that can be stored and manipulated in a SQL Server database...<br />Contents<br /> TOC quot;
1-2quot;
Introduction PAGEREF _Toc205575287 1<br />Comprehensive Spatial Support PAGEREF _Toc205575288 2<br />Spatial Models PAGEREF _Toc205575289 2<br />SQL Server 2008 Spatial Data Types PAGEREF _Toc205575290 4<br />Spatial Data Type Methods PAGEREF _Toc205575291 4<br />High Performance Spatial Data Capabilities PAGEREF _Toc205575292 6<br />Built-in Spatial Views PAGEREF _Toc205575293 7<br />Location-Aware Application Extensibility PAGEREF _Toc205575294 8<br />Importing Spatial Data PAGEREF _Toc205575295 8<br />Using Spatial Data PAGEREF _Toc205575296 9<br />Conclusion PAGEREF _Toc205575297 10<br />Introduction<br />Today’s information workers and consumers deal with massive amounts of information of different kinds, from traditional tables of business data in spreadsheets and databases, to online media-based data such as video, photographs, and music. The recent trend towards mash up solutions in which information and content from multiple sources is combined to create versatile online applications is indicative of the way that computer users use highly integrated solutions to make sense of the vast amount of information that is available to them.<br />At the same time, advances in technology have led to the proliferation of geographical services and devices, including online mapping solutions such as Microsoft® Virtual Earth™, and inexpensive global positioning system (GPS) solutions. Technology that was once the preserve of geographic information system (GIS) specialists is now widely available to everyone.<br />These two factors bring new expectations and opportunities for software applications. The ubiquity of geographical services, and the increasing sophistication with which users consume data means that spatial information is just another component to be incorporated into a solution and used as a basis for making better decisions and providing higher value services.<br />Spatial data can be used in many ways, as the following list of examples demonstrates:<br />A retailer Web site can display the locations of all stores as pins on a map, and find the nearest store to a given zip code<br />A sales manager can define geographic sales regions, and use them to match customers to sales representatives and perform analysis of sales performance.<br />An architect can create plans for a new building, and overlay those plans onto a map of the proposed site.<br />A driver can find the distance between two locations, and plan a route.<br />A real estate agent can quickly identify properties that match a client’s requirements, such as houses over 20,000 square feet in size that are on the shore of Lake Washington.<br />A mobile application can find all gas stations within 10 miles of a given location.<br />These examples represent only a few of the possibilities created by the integration of spatial data into software applications.<br />SQL Server 2008 R2 provides support for geographical data through the inclusion of new spatial data types, which you can use to store and manipulate location-based information. The spatial support in SQL Server 2008 R2 can help users to make better decisions through analysis of location data in scenarios such as:<br />Consumer-focused location-based information<br />Customer-base management and development<br />Environmental-related data impact, analysis, and planning<br />Financial and economic analysis in communities<br />Government-based planning and development analysis<br />Market segmentation and analysis<br />Scientific research study design and analysis<br />Real-estate development and analysis<br />This whitepaper provides a high-level introduction to the comprehensive spatial data support in SQL Server 2008 R2, and describes its high-performance spatial capabilities and location-aware application extensibility.<br />Comprehensive Spatial Support<br />SQL Server 2008 R2 provides comprehensive spatial support through new data types. To understand how you can use these data types to store location-based data, you first need to understand a little of how spatial data, and in particular geospatial data, works.<br />Spatial Models<br />Spatial data is used to represent points, lines, and areas on a surface. Most commonly, these elements relate to actual physical locations on Earth, so can be described a geospatial data. Most of us are familiar with this concept through the use of globes and maps, which generally show multiple geographic features and their relative locations.<br />Geodetic Spatial Models<br />The problem with describing a location on a planetary surface is that planets are not flat. Earth is a very complex object that can be reasonably approximated by an oblate spheroid, a (slightly) flattened sphere. An accurate representation of the Earth is usually manifested as a globe, in which locations on the surface of the planet are described in terms of their latitude and longitude, which is measured in degrees from the equator and the international date-line respectively. <br />This approach to modeling geographic locations is called a geodetic model, and provides an accurate way to define locations and objects on a globe as shown in Figure 1. There are a number of different geodetic models in use throughout the world, including the Airy 1830 ellipsoid used in the United Kingdom’s Ordnance Survey geographic system, and the WGS84 ellipsoid used by the world’s GPS solutions.<br />Figure 1: A geodetic model<br />Planar Spatial Models<br />While a geodetic model provides the most accurate way to represent geographic features, working with an ellipsoid and taking planetary curvature into account when calculating distances was extremely difficult before computing when people had to work with flat maps. Historically, it has been much easier to work with two-dimensional surfaces, or planes, so it is common to find location-based data represented in various flat (planar) models. To work with geospatial data on a flat two dimensional surface, a projection is created to flatten the geographical objects on the spheroid. As with geodetic models, there are many mathematical models used to project the geographical features of Earth onto a flat surface, including the Mercator projection, the Peters projection, and the Lambert Conformal Conic projection. Figure 2 shows a planar model of the Earth based on the Mercator projection.<br />Figure 2: A planar model<br />Regardless of which projection is used, converting geographical data from a spheroid to a flat surface always results in some distortion of the shape, size, or position (or all three) of the geographic features in the resulting map, which is why in the projection shown in Figure 2, Greenland is shown as being almost the same size as the United States of America, even though in reality its land mass is much smaller. Generally, the larger the surface area being projected, the more distortion occurs – with the features at the furthest edges of the map exhibiting more distortion than those at the center. For this reason, planar models work best for small geographical areas such as individual countries, states, and towns, or for non-projected spatial surfaces such as interior floor plans.<br />SQL Server 2008 R2 Spatial Data Types<br />SQL Server 2008 R2 provides the geography data type for geodetic spatial data, and the geometry data type for planar spatial data. Both are implemented as Microsoft .NET Framework Common Language Runtime (CLR) types, and can be used to store different kinds of geographical elements such as points, lines, and polygons. Both data types provide properties and methods that you can use to perform spatial operations such as calculating distances between locations and finding geographical features that intersect one another (such as a river that flows through a town.)<br />The geography Data Type<br />The geography data type provides a storage structure for spatial data that is defined by latitude and longitude coordinates. Typical uses of this kind of data include defining roads, buildings, or geographical features as vector data that can be overlaid onto a raster-based map that takes into account the curvature of the Earth, or for calculating true great circle distances and trajectories for air transport where the distortion inherent in a planar model would cause unacceptable levels of inaccuracy.<br />The geometry Data Type<br />The geometry data type provides a storage structure for spatial data that is defined by coordinates on an arbitrary plane. This kind of data is commonly used in regional mapping systems, such as the state plane system defined by the United States government, or for maps and interior floor plans where the curvature of the Earth does not need to be taken into account.<br />The geometry data type provides properties and methods that are aligned with the Open Geospatial Consortium (OGC) Simple Features Specification for SQL and enable you to perform operations on geometric data that produce industry-standard behavior.<br />Spatial Data Type Methods<br />Both spatial data types in SQL Server 2008 R2 provide a comprehensive set of instance and static methods that you can use to perform queries and operations on spatial data. For example, the following code sample creates two tables for a city mapping application; one contains geometry values for the districts in the city, and the other contains geometry values for the streets in the city. A query then retrieves the city streets and the districts that they intersect.<br />CREATE TABLE Districts <br /> (DistrictId int IDENTITY (1,1),<br />DistrictName nvarchar(20),<br /> DistrictGeo geometry);<br />GO<br />CREATE TABLE Streets <br /> (StreetId int IDENTITY (1,1),<br />StreetName nvarchar(20),<br /> StreetGeo geometry);<br />GO<br />INSERT INTO Districts (DistrictName, DistrictGeo)<br />VALUES ('Downtown',<br />geometry::STGeomFromText<br />('POLYGON ((0 0, 150 0, 150 150, 0 150, 0 0))', 0));<br />INSERT INTO Districts (DistrictName, DistrictGeo)<br />VALUES ('Green Park',<br />geometry::STGeomFromText<br />('POLYGON ((300 0, 150 0, 150 150, 300 150, 300 0))', 0));<br />INSERT INTO Districts (DistrictName, DistrictGeo)<br />VALUES ('Harborside',<br />geometry::STGeomFromText<br />('POLYGON ((150 0, 300 0, 300 300, 150 300, 150 0))', 0));<br />INSERT INTO Streets (StreetName, StreetGeo)<br />VALUES ('First Avenue',<br />geometry::STGeomFromText<br />('LINESTRING (100 100, 20 180, 180 180)', 0))<br />GO<br />INSERT INTO Streets (StreetName, StreetGeo)<br />VALUES ('Mercator Street', <br />geometry::STGeomFromText<br />('LINESTRING (300 300, 300 150, 50 50)', 0))<br />GO<br />SELECT StreetName, DistrictName<br />FROM Districts d, Streets s<br />WHERE s.StreetGeo.STIntersects(DistrictGeo) = 1<br />ORDER BY StreetName<br />The results from this query are shown in the following table.<br />Query Results<br />StreetNameDistrictNameFirst AvenueDowntownFirst AvenueHarborsideMercator StreetDowntownMercator StreetGreen ParkMercator StreetHarborside<br />High Performance Spatial Data Capabilities<br />The spatial data types in SQL Server 2008 R2 are implemented as CLR system types. SQL Server 2008 R2 increases the maximum size for CLR types in the database from the 8000 bytes limit that was imposed in SQL Server 2005 to 2GB, which makes it possible to store extremely complex spatial data elements, such as polygons, which are defined by a large number of points.<br />By storing spatial data in relational tables, SQL Server 2008 R2 makes it possible to combine spatial data with any other kind of business data; this removes the need to maintain a separate, dedicated spatial data store and enables high performance queries that do not need to combine data from multiple external sources.<br />Performance of queries against spatial data is further enhanced by the inclusion of spatial index support in SQL Server 2008 R2. You can index spatial data with an adaptive multi-level grid index that is integrated into the SQL Server database engine. Spatial indexes consist of a grid-based hierarchy in which each level of the index subdivides the grid sector that is defined in the level above. A conceptual model of a spatial index is shown in Figure 3.<br />Figure 3: A spatial index<br />The SQL Server query optimizer makes cost-based decisions on which indexes to use for a given query, and because spatial indexes are an integral part of the database engine, SQL Server can make cost-based decisions about whether or not to use a particular spatial index, just like any other index.<br />Built-in Spatial Views<br />Use the new spatial results tab to easily view spatial query results directly from within SQL Server Management Studio. This tab offers simple projection and zoom/pan capabilities for quick investigation.<br />Figure 4: A spatial views tab within Management Studio<br />Location-Aware Application Extensibility<br />The geography and geometry data types are supported in the various SQL Server 2008 R2 editions that scale from single-user desktop applications to enterprise-level data stores and enable you to build geospatial solutions of any scale. This broad support brings spatial data capabilities to all kinds of applications without the need for expensive proprietary geospatial solutions.<br />Importing Spatial Data<br />The geography and geometry data types include methods for importing and exporting data in the Well Known Text (WKT) and Well Known Binary (WKB) formats for geographic data that are defined by the OGC, as well as the commonly used Geographic Markup Language (GML) format, which makes it easy to import geographic data from source that supports these standards. Geographical data is readily available from a number of government and commercial sources, and can be exported relatively easily from many existing GIS applications and GPS systems. Microsoft maintains close relationships with a number of third-party GIS vendors and geospatial data solution providers, which helps to ensure strong compatibility between SQL Server 2008 R2 and a wide range of industry-proven tools and utilities for importing, exporting, and manipulating spatial data.<br />Using Spatial Data<br />As already demonstrated in this whitepaper, the geography and geometry data types provide methods that you can use to perform spatial operations on your data. Because these data types are implemented as .NET CLR types, you can easily create client applications that consume spatial data from SQL Server through Microsoft data programmability technologies and use client-side managed code to call methods on instances of the spatial types. This enables you to build powerful applications to work with your spatial data and integrate it with other location-aware applications and services such as Virtual Earth.<br />For example, Figure 4 shows an application in which spatial data from SQL Server 2008 R2 is integrated with Virtual Earth. The application shows the census blocks in a ZIP Code region with the number of restaurants computed. The number of restaurants in each block, relative to the size of the block yields a density value, which appears in the display as a region shaded from white (low density) to red (highest density).<br />Figure 5: Spatial data integrated with Virtual Earth<br />Conclusion<br />As the integration of geospatial information into applications becomes more prevalent, application developers will increasingly require database systems that can store and manipulate spatial data. With the introduction of the geography and geometry data types, SQL Server 2008 R2 provides a comprehensive, high-performance, and extensible data storage solution for spatial data, and enables organizations of any scale to integrate geospatial features into their applications and services.<br />For more information:<br />Microsoft SQL Server 2008http://www.microsoft.com/sql<br />Please give us your feedback:<br />Did this paper help you? Tell us on a scale of 1 (poor) to 5 (excellent), how would you rate this paper and why have you given it this rating? For example:<br />Are you giving it a high rating because it has good examples, excellent screenshots, clear writing, or another reason? <br />Are you giving it a low rating because it has poor examples, fuzzy screenshots, unclear writing?<br />This feedback will help us improve the quality of white papers we release. Send feedback.<br />