This discussion is archived. I am creating a signs inventory table in our Oracle database to be used as a data warehouse for our signs inventory. The Lon and Lat fields along with some other data will be populated from a MS access file exported from our Geodatabase. What data type should these two columns be defined in the new Oracle database table??? I have the same question Show 0 Likes 0.
This content has been marked as final. Show 4 replies. Depending on the type of signs you're mapping stop signs or billboards? But hey, space is cheap! Hi Noel, Thanks for your response. So, what would you recommend?
BTW, I like your example. Thanks again for all the help. I am hoping to receive some answers to my questionsUse a 2d index for data stored as points on a two-dimensional plane. The 2d index is intended for legacy coordinate pairs used in MongoDB 2.
For more information on geospatial queries, see Geospatial Queries. Starting in MongoDB 4. If a 2d index does not exists, then MongoDB looks for a 2dsphere index to use. You cannot use a 2d index as a shard key when sharding a collection. However, you can create a geospatial index on a sharded collection by using a different field as the shard key. The 2d index supports calculations on a flat, Euclidean plane. The 2d index also supports distance-only calculations on a sphere i. A 2d index can reference two fields.
Mapping Terms and Concepts from SQL to MongoDB
The first must be the location field. A 2d compound index constructs queries that select first on the location field, and then filters those results by the additional criteria.
A compound 2d index can cover queries. If a document lacks a 2d index field or the field is null or an empty arrayMongoDB does not add an entry for the document to the 2d index. For inserts, MongoDB inserts the document but does not add to the 2d index. For a compound index that includes a 2d index key along with keys of other types, only the 2d index field determines whether the index references a document.Power BI is expanding self-service data prep to help business analysts extract insights from big data and introducing enterprise BI platform capabilities.
With recent updates, Power BI has enabled connectivity to more data sources than ever before. With the CData connectors, you can access MongoDB data quickly faster than you can with any other connector and easily, leveraging the built-in modeling and data flattening features of the connector to create a table-like model of your schema-less data, ready to be viewed, reported and analyzed from Power BI — no code or data curation required.
Our connectors provide the fastest and most flexible support for NoSQL data integration, allowing you to interact with NoSQL databases as if they were a relational database, leveraging the flexibility of NoSQL data storage with the familiarity and utility of SQL queries.
The options range from free-form queries to horizontal and vertical flattening to custom schema definitions. You can download a truncated version of the dataset and import it into your MongoDB instance using the following command:. With this document in mind, we configure the CData Connector to build a schema based on the hierarchy of the data, using dot notation and array indexes to drill down into the data.
Drilling down into the MongoDB data to build this schema is as easy as setting a few configuration properties and then modifying the generated file to change the names of the fields. With the connection properties configured, click Test Connection to ensure the properties are properly configured and to generate the schema file. Open the generated schema file restaurants. Each column has specific attributes like whether or not the column is read-only, it's source datatype, and more.
At this point, you will have access to the restaurants data as defined by the schema file in Power BI, as seen in the fields menu in Power BI. There are many distinguishing parts of the restaurant data. Thanks to built-in location mapping, we can build a map of the restaurants using the latitude and longitude.
We can also view the restaurants based on Borough or cuisine. For starters, let's build the map. Now you have a Map chart that plots each restaurant, colored by the borough and with size based on the average score.
Now you have a Stacked column chart for the restaurants, where each borough is a column and each column is divided by cuisine. We went through the process of building a report with two more tables to show the numbers of restaurants by borough and cuisine. In this report, you can use the focus functionality of Power BI to select a Borough or a cuisine type or both and drill down to those data points associated with your selections. Get started with a free, day trial of any of the Power BI Connectors.Latitude and Longitude with the Geography Data Type - SQL Training Online
For more videos, head over to our YouTube channel. As always, let us know if you have any questions during your evaluation. Our world-class CData Support Team is always available to help. View All Products. View All Drivers. Support Resources. Order Online Contact Us. About Us. Testimonials Press Contact Us Resellers. Sign up for our Newsletter! Company Email :. Phone Email Text.This post is a quick and simple introduction to Geospatial feature of MongoDB 2.
Storing Geospatial Informations As you know you can store any type of data, but if you want to query them you need to use some coordinates, and create index on them. MongoDB supports three types of indexes for GeoSpatial queries: 2d Index : uses simple coordinate longitude, latitude. As stated in the documentation: The 2d index is intended for legacy coordinate pairs used in MongoDB 2.
For this reason, I won't detail anything about this in this post.
It is today less used by applications and I will not describe it in this post. It is also possible to mix all these in a single document using a GeometryCollection. Now what? Let's store data! For example if you want to store a document about JFK Airport with its location you can run the following command: db.
For this we need a sample dataset. I have created one using some open data found in various places. Let's install the dataset: Download it from here Unzip geo. Let's now look at some concrete examples. Inclusion Find all the airports in California. So the query is using the "California MultiPolygon" and looks in the airports collection to find all the airports that are in these polygons. This looks like the following image on a map: You can use any other query features or criteria, for example you can limit the query to international airport only sorted by name : db.
You can run a query with the explain to see what's going on.
Intersection Suppose you want to know what are all the adjacent states to California, for this we just need to search for all the states that have coordinates that "intersects" with California. Let's find all the international airports that are located at less than 20km from the reservoir in NYC Central Park. Conclusion In this first post about geospatial feature you have learned: the basic of GeoJSON how to query documents with inclusion, intersection and proximity criteria.
You can now play more with this for example integrate this into an application that expose data into some UI, or see how you can use the geospatial operators into a aggregation pipeline. Posted by Tug Grall at PM.Encoding channels are the building blocks of your visualizations. These channels are the means by which Charts translates data from your collection to the visualization. The encoding process dictates how that data appears in the chart based on the channel type selected.
Each encoding channel type provides different capabilities for processing and visualizing your data. Each channel type accepts distinct data types e. The following table describes each encoding channel type in MongoDB Charts:. Each unique value from this field in the collection is used as a chart category. The Category encoding channel provides the following additional visualization options:.
When you create a chart using a geopoint channel type, MongoDB Charts renders a map to visualize your data. Geopoint channel types are used in the Coordinates channel on Geospatial Scatter and Geospatial Heatmap chart types.
For more information on the geopoint encoding channel type, refer to the Geopoint Channel Type section. Dragging a field onto an aggregation encoding channel allows you to use an aggregation pipeline operator on that field.
The aggregation operators available in the chart builder change based on the data type of the selected field. MongoDB Charts categorizes the field data type as either a numberstringor date and displays appropriate aggregation options accordingly.
Data Types for Storing Latitude and Longitude Coordinates
We see from the chart that the most common landslide trigger is Downpour followed by Rain. Geospatial charts visualize data that references specific geographic locations. Charts signifies these fields with a geoglobe icon icon. You can map these fields directly to a geopoint channel type by dragging them onto an appropriate geopoint channel type in the chart builder.
Charts automatically determines the latitude and longitude values from the field and adds a point to the map for each document. You can also assign numeric coordinates or arrays to a geopoint channel type to specify latitude and longitude not stored as GeoJSON Points. For details on this process, see the following section. For additional information and examples on Geospatial Scatter and Heatmap charts which utilize geopoint channel types, refer to the relevant chart type pages.
MongoDB Charts populates the Fields pane of the chart builder by randomly sampling documents from the selected data source. As a result, Charts may not display all fields from documents in the data source if the field is not present on all documents. To surface a specific field in the chart builder, you can use the Query Bar to define certain fields which must appear in the sampled documents. The following table describes each encoding channel type in MongoDB Charts: Encoding Channel Type Description Value A point on the chart is created from the chosen field for each document in the collection.
You may only assign fields with a numeric or date type to a Value channel. Category Each unique value from this field in the collection is used as a chart category.My last post described a trap for the unwary when using latitude and longitude values stored as the float data type in SQL Server, which left me wondering why anyone would use the float data type for this purpose.
I questioned whether the float data type offers any advantages over the exact numeric types decimal and numeric for storing latitude and longitude values.
I could think of only two factors that would affect the choice of data type here: 1 accuracy and precision and 2 storage requirements. After some research and testing, I concluded that an exact numeric data type, decimal 9,6will be a better choice for storing latitude and longitude values in most cases because it provides a reasonable degree of accuracy and precision and requires less storage space than the float data type.
Accuracy and precision, as I use those terms here, define how well a measured value represents the actual or true value. Accuracy means the degree of closeness of a measurement to the actual or true value. Precision refers to the number of significant digits used to represent a measured value.
The precision of a measured value limits the degree of accuracy that can be ascribed to a measurement, and presenting a measured value with greater precision than warranted can lead to overconfidence in the accuracy of the measurement. For latitude and longitude values essentially measured values to appropriately represent the actual values, then, the data type used to store them must accommodate a reasonable degree of accuracy and precision.
The accuracy of latitude and longitude values depends on the accuracy of the system used to determine them. The well-known Global Positioning System GPS uses the transit time of radio signals transmitted by orbiting satellites to determine latitude and longitude.
Federal Aviation Administration determined that GPS coordinates were accurate to about 10 meters or less GPS augmentation systems can boost this accuracy to a few centimeters. We can use this accuracy figure to determine the precision with which we can represent latitude and longitude values determined using GPS. Generally, the number of significant digits used to represent a measurement should be determined by the limit of accuracy of the measuring device.
At the geographic center of the 48 contiguous United States0. Accordingly, six decimal places would be appropriate for representing GPS measurements of latitude and longitude. We now know that we need a data type that can represent values between and the range of longitude values; latitude values range from to 90 to six decimal places.
All other things being equal, storing the same data in less storage space is a good thing. If we can accomplish our purpose with fewer bytes, generally we should. While saving three bytes of storage per row or more likely, six bytes per row, since latitude and longitude values usually come in pairs may not seem like much, using those extra bytes to store latitude and longitude values as float 53 buys us almost nothing and costs us at least something compared to storing those values as decimal 9,6.
Just to verify that decimal 9,6 will suffice to store latitude and longitude values for almost all purposes, I put together a quick example: calculating the distance from John F. First, I calculated the distance using the float data type with nine decimal places I obtained the latitude and longitude coordinates of each airport to six decimal places and added three pseudorandom digits to each :.
The calculated distance varies by only 27 cm between the float data type calculation with nine decimal places and the decimal 9,6 calculation.
I suspect that there are very few measurement systems in the world that are precise enough at distances of almost 4, km that this difference could be considered significant, so the more storage-efficient decimal 9,6 data type will be preferable to the float 53 data type for storing latitude and longitude coordinates in most cases. You are commenting using your WordPress.
You are commenting using your Google account. You are commenting using your Twitter account. You are commenting using your Facebook account. Notify me of new comments via email. Notify me of new posts via email. Accuracy and precision of latitude and longitude values Accuracy and precision, as I use those terms here, define how well a measured value represents the actual or true value. Storage space All other things being equal, storing the same data in less storage space is a good thing.
A quick example Just to verify that decimal 9,6 will suffice to store latitude and longitude values for almost all purposes, I put together a quick example: calculating the distance from John F. First, I calculated the distance using the float data type with nine decimal places I obtained the latitude and longitude coordinates of each airport to six decimal places and added three pseudorandom digits to each : I then calculated the distance using the decimal 9,6 data type: The calculated distance varies by only 27 cm between the float data type calculation with nine decimal places and the decimal 9,6 calculation.
Share this: Twitter Facebook LinkedIn. Like this: Like Loading Leave a Reply Cancel reply Enter your comment here Fill in your details below or click an icon to log in:.The examples in this document use the zipcodes collection. This collection is available at: media. Use mongoimport to load this data set into your mongod instance. Each document in the zipcodes collection has the following form:.
All of the following examples use the aggregate helper in the mongo shell. The aggregate method uses the aggregation pipeline to processes documents into aggregated results. An aggregation pipeline consists of stages with each stage processing the documents as they pass along the pipeline. Documents pass through the stages in sequence. The aggregate method in the mongo shell provides a wrapper around the aggregate database command.
See the documentation for your driver for a more idiomatic interface for data aggregation operations. The following aggregation operation returns all states with total population greater than 10 million:. The totalPop field is a calculated field that contains the total population of each state.
The equivalent SQL for this aggregation operation is:. The following aggregation operation returns the smallest and largest cities by population for each state:.
This operation does not alter the documents. The stage also calculates the following four fields for each state. A city can have more than one zip code associated with it as different sections of the city can each have a different zip code.