WO2016145283A2 - System for sound evaluation in a designated area - Google Patents

Abstract

A method and system to provide an aggregate noise level value for a specific location. The system is based on distributed computation by a noise evaluation application. A database interface is coupled to a plurality of relevant databases. A user interface device including an input device accepts the request of noise data relating to the specific location. The application aggregates relevant data in proximity to the requested location from the database interface. An aggregate noise level value is calculated for the requested location. The value is stored in a database and returned to the user interface device.

Description

SYSTEM FOR SOUND EVALUATION IN A DESIGNATED AREA

TECHNICAL FIELD

[0001] The disclosure relates generally to systems and methods for noise evaluation. In particular, the disclosure relates to a system for determining noise levels of a specified geographical area.

PRIORITY

[0002] This application claims priority to U.S. Provisional Application Serial No. 62/131,713 filed on March 11, 2015. That application is hereby incorporated by reference in its entirety.

BACKGROUND

[0003] In general, information about a surrounding area is desirable for property valuation determination. For example, location of property in proximity to desired transportation infrastructure, amenities, and services may increase property value. Another consideration is whether an area is in proximity to noise sources as people generally prefer more quiet surroundings for residential dwellings. The level of surrounding noise is often a significant complaint of residents and thus is a factor that affects quality of life. The level of surrounding noise is therefore a consideration for real estate valuation.

[0004] Data relating to noise levels has not been systematically available to interested parties such as property owners and potential property buyers. Such information is largely dependent on the location of property but has not been historically evaluated in any systematic manner. Recently, with additional data that has come available from diverse sources such as traffic databases, municipal databases, mapping databases, and geographical databases, it is possible to provide information relating to surrounding noise in a geographic area. Such data must be analyzed and combined with other data to accurately determine the noise level of a certain area. However, current noise modeling software tools focus on individual projects such as a study for a school, then a highway expansion, then a city study, etc. Such software tools require certain inputs from a user, making the software harder to adapt to other locations.

[0005] Thus, there is a need for a noise evaluation system that provides the ability to determine noise levels around a specific location within a wide ranging area. There is a further need for an automated noise evaluation system that minimizes the amount of human inputs involved. There is a further need for a system that provides a uniform process for all parts of a wide geographical area that pulls relevant data from relatively large datasets. There is also a need to make sound data readily available to users in an accessible manner.

SUMMARY

[0006] One example is a method and system to provide an aggregate noise level value for a specific location. The system is based on distributed computation by servers running a noise evaluation application. A database interface is coupled to a plurality of relevant databases. A user interface device including an input device accepts the request of noise data relating to the specific location. The application aggregates relevant data in proximity to the requested location from the database interface. The application compiles groups of data relating to the requested location to determine the vehicle noise value, the airport noise value, and the local commercial noise value. An aggregate noise level value is calculated for the requested location. The value is stored in a database and returned to the user interface device.

[0007] Another disclosed example is a system to build a sound data map of a geographic region. The system includes a database interface coupleable to a plurality of databases including at least one geographic database and at least one database having noise data. A controller aggregates noise data in proximity to a predetermined number of locations within the geographic area from the database interface. The controller generates a sound data map for the geographic region. A storage device stores the generated sound data map for the geographic region

[0008] Another disclosed example is a method to build a sound data map for a selected region. An area into squares having a pre-determined dimension. A region within the area is selected. A local noise score, a traffic noise score and an airport noise score is determined for each of the squares within the selected region. A sound score overlay for the region is determined. The sound score overlay for the region is stored in the form of a sound data map.

[0009] Additional aspects of the invention will be apparent to those of ordinary skill in the art in view of the detailed description of various embodiments, which is made with reference to the drawings, a brief description of which is provided below.

BRIEF DESCRIPTION OF FIGURES

[0010] Exemplary embodiments are illustrated in referenced figures. It is intended that the embodiments and figures disclosed herein are to be considered illustrative rather than restrictive.

[0011] FIG. 1 is a block diagram of a cloud-based data gathering and compilation system for noise evaluation of a specified area;

[0012] FIG. 2 is a flow diagram of the process of the system in FIG. 1 to provide noise evaluation for a geographic area;

[0013] FIG. 3 is an image of an example output in relation to noise data for a specific geographical region on a user device from the system in FIG. 1 ;

[0014] FIGs. 4A-4B are images of example specific location outputs for a user device;

[0015] FIGs. 5A-5B are images of another example of specification location outputs for a user device;

[0016] FIG. 6 is a flow diagram of the process of the system in FIG. 1 to pre-generate a sound map for a selected region;

[0017] FIG. 7 is an example image of a larger area sound map; and

[0018] FIG. 8 is an example computing device for the example system in FIG. 1.

DETAILED DESCRIPTION

[0019] FIG. 1 is a block diagram of a sound evaluation system 100 that may be used to determine the noise levels for certain geographic areas or specific building locations. The software may incorporate data relating to noise from vehicle traffic, air traffic, and local sources such as stores, gas stations, schools, etc. Other sources may include public transit, railroad, and frequent helicopter routes. The noise evaluation system 100 includes interfaces to a pool of data sources 102. The data sources 102 may include city, state, county, and U.S. Geological Survey (USGS) data relating to geography and/or sound. Other sources such as specific vendors collecting relevant data and other public sources such as traffic volumes, traffic speeds, parcel outlines, 2D and 3D building outlines, public transportation routes and schedules, latitude/longitude descriptions of roads, airport locations, airport runway directions, flight volumes, and aircraft types. Other example sources also include directories of the location and types of points of interest such as stores, automotive businesses, schools, medical facilities, etc. This information is obtained in a number of ways. For example, road information may be obtained from HERE of Chicago, Illinois, as one large package; point of interest information may be obtained from Factual of Los Angeles, California; and LIDAR data may be obtained from the USGS website. Some information may be obtained from the Internet, such as airport information from Airnav. Building outline and parcel files for many cities, counties, and even states are often available on the respective municipality's geographic information website. Wikipedia may be used for its list of airports throughout the United States. Public transit information may be in "General Transit Feed Specification" format and may be obtained, for example,

Of course, other data sources and information retrieval may be used.

[0020] Data is pulled from the data sources 102 in groups for each component of an aggregate noise level value that is computed for a particular location. In this example, the groups of data are used to determine the vehicle noise value, the airport noise value, and the local commercial noise value. The data includes a vehicle traffic data group 104, an airplane data group 106, and a local sources data group 108. The vehicle traffic data group 104 includes roads, traffic volume, and speed data according to time of week, topography of different areas, and 2D and 3D data relating to structures such as buildings. The airplane data group 106 includes summary information relating to airport locations, runways, passenger volume, flight volume, and aircraft types. The local sources data group 108 includes noise data from categories of buildings including stores, gas stations, car repair, schools, police, fire, hospitals, bars, restaurants, etc. Each category has a weight for the typical noise level associated with it. The weight for each point of interest is multiplied by a decay value that is a function of the distance from the point of interest to the building being evaluated, or other aggregate distance functions may be used. A large scale density map is created using these values. The weight may be set according to the type of point, such as a restaurant or a store, and the size of the parking facility. The local commercial noise value may also incorporate the operating hours of noise sources, such as bars or gas stations. Alternatively, the values may then be summed against a weight so that as the number of sources increases linearly, the local commercial noise value increases logarithmically.

[0021] The cloud 110 includes servers 112 and databases 114 that allow the data for a specific area, including a requested location, to be compiled from the groups 104, 106, and 108. The servers 112 run an application that determines dynamic distributed noise population computation as will be explained below. The application also allows the output of a 3D map showing the aggregate noise evaluation scores for a user. The resulting output noise evaluation data for a particular area or building may be stored in a results database 114. A management entity 116, such as HowLoud, Inc., controls the noise evaluation system 100. The management entity 116 controls the noise evaluation system 100 to compute vehicle noise estimates and aggregate noise level values for buildings in urban regions with tens of thousands to several million buildings. These results are then made available to users to query. For regions that are not pre-generated, a user query for a particular address initiates the computation for that address, which takes at most several seconds to complete. The result is then entered to the database 114 and returned to the user.

[0022] User devices 118 may access the cloud 110 through a network connection, such as through an Internet connectable interface, to access the noise evaluation application running on the cloud servers 112. The user devices 118 may include multiprocessor systems, microprocessor-based or programmable consumer electronics, and the like. As such, user devices 118 running the application described below may range widely in terms of capabilities and features. The computing devices include an API that allows access to the output noise evaluation data. Human users may access the noise evaluation application via user computing devices. The user computing devices may also be a laptop computer, a desktop computer, a work station, or other computer. The user computing devices may also include virtually any preferably mobile computing device that is configured to send and receive information over a wireless capable network, such as the cloud network. In this example, the user computing devices are web-enabled and may run browser software for the presentation of web pages to the user. Mobile user devices may include portable devices such as cellular telephones, smart phones, display pagers, radio frequency (RF) devices, infrared (IR) devices, global positioning devices (GPS), Personal Digital Assistants (PDAs), handheld computers, wearable computers, tablet computers, integrated devices combining one or more of the preceding devices, and the like. As will be explained below, a user device may query an address or geographic coordinates via the API and receive output noise evaluation data for the address or geographic coordinates. The noise evaluation data may either be computed via the application running on the servers 112 or it may be retrieved from the database 114 if such data has already been determined for the requested address or geographic coordinates.

[0023] Of course, the application may also be run on a dedicated set of servers on a private network or on the user device directly. The noise data may be made available to other websites using the API. For example, a real estate website server may use the API to retrieve the noise information for the properties it lists and display that information on the listing page. Thus, the application will allow automated queries from another website.

[0024] The servers 112 may include various applications that may be accessed to further refine noise- based information to potential users for the noise evaluation application. The servers 112 communicate through the cloud network to computing devices accessible by users to run the noise evaluation application. It is to be understood that two or more computing systems or devices may be substituted for any one of the computing systems or devices in the noise evaluation system 100. Accordingly, principles and advantages of distributed processing, such as redundancy, replication, and the like, also can be implemented, as desired, to increase the robustness and performance of the devices and systems of the noise evaluation system 100. The servers 112 are also coupled to a database storage server or servers that manage data in the database 114.

[0025] In this example, certain human operated user devices are generally portable or mobile devices that include geo-referencing hardware and software that allow their location to be known to both their users and the servers 112. The geo-referencing hardware and software for locating a particular device in this example is based on the Global Positioning System (GPS), but other geo-referencing systems may be used.

[0026] As exampled below, the human- operated user devices may include a browser application enabled to receive and to send wireless application protocol messages (WAP), and/or wired application messages, and the like. In one example, the browser application is enabled to employ Hyper Text Markup Language (HTML), Dynamic HTML, Handheld Device Markup Language (HDML), Wireless Markup Language (WML), WMLScript, JavaScript, Extensible HTML (xHTML), Compact HTML (CHTML), and the like, to display and/or send digital information.

[0027] The user devices may also include at least one client application that is configured to receive control data and/or content from another computing device via a network transmission. The client application may include a capability to provide and receive textual content, graphical content, video content, audio content, and the like. Moreover, the user devices may be further configured to communicate and/or receive a message, such as through a Short Message Service (SMS), direct messaging (e.g., Twitter), e-mail, Multimedia Message Service (MMS), instant messaging (IM), internet relay chat (IRC), mIRC, Jabber, Enhanced Messaging Service (EMS), text messaging, Smart Messaging, Over the Air (OTA) messaging, or the like, between or with another computing device, and the like.

[0028] The cloud network is configured to allow communications between one computing device and another computing device. The cloud network may be enabled to employ any form of computer readable media for communicating information from one electronic device to another. On an interconnected set of LANs, including those based on differing architectures and protocols, a router and/or gateway device acts as a link between LANs, enabling messages to be sent between computing devices. Also, communication links within LANs typically include twisted wire pair or coaxial cable, while communication links between networks may utilize analog telephone lines; full or fractional dedicated digital lines including Tl, T2, T3, and T4; Integrated Services Digital Networks (ISDNs); Digital Subscriber Lines (DSLs); wireless links including satellite links; or other communication links known to those of ordinary skill in the art. Furthermore, remote computers and other related electronic devices can be remotely connected to either LANs or WANs via a modem and temporary telephone link.

[0029] The cloud network may further include any of a variety of wireless sub-networks that may further overlay stand-alone ad-hoc networks, and the like, to provide an infrastructure-oriented connection. Such sub-networks may include mesh networks, Wireless LAN (WLAN) networks, cellular networks, and the like. The cloud network may also include an autonomous system of terminals, gateways, routers, and the like connected by wireless radio links or wireless transceivers. These connectors may be configured to move freely and randomly and organize themselves arbitrarily, such that the topology of the cloud network may change rapidly and arbitrarily.

[0030] The network may further employ a plurality of access technologies including 2nd (2G), 2.5, 3rd (3G), 4th (4G) generation radio access for cellular systems; WLAN; Wireless Router (WR) mesh; and the like. Access technologies such as 2G, 3G, 4G, and future access networks may enable wide area coverage for mobile devices with various degrees of mobility. For example, the network may enable a radio connection through a radio network access such as Global System for Mobile communication (GSM), General Packet Radio Services (GPRS), Enhanced Data GSM Environment (EDGE), Wideband Code Division Multiple Access (WCDMA), CDMA2000, and the like. The network may also be constructed for use with various other wired and wireless communication protocols, including TCP/IP, UDP, SIP, SMS, RTP WAP, CDMA, TDMA, EDGE, UMTS, GPRS, GSM, UWB, WiMax, IEEE 802. l lx, and the like. In essence, the cloud network may include virtually any wired and/or wireless communication mechanisms by which information may travel between one computing device and another computing device, network, and the like. [0031] The process of evaluating noise for a specific location in a geographic area will now be described with reference to FIG. 1 in conjunction with the flow diagram shown in FIG. 2. The flow diagram in FIG. 2 is representative of example machine readable instructions for gathering noise related data and producing noise evaluation data by the noise evaluation system 100 in FIG. 1. In this example, the machine readable instructions comprise an algorithm for execution by: (a) a processor, (b) a controller, and/or (c) one or more other suitable processing device(s). The algorithm may be embodied in software stored on tangible media such as, for example, a flash memory, a CD-ROM, a floppy disk, a hard drive, a digital video (versatile) disk (DVD), or other memory devices, but persons of ordinary skill in the art will readily appreciate that the entire algorithm and/or parts thereof could alternatively be executed by a device other than a processor and/or embodied in firmware or dedicated hardware in a well-known manner (e.g., it may be implemented by an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable logic device (FPLD), a field programmable gate array (FPGA), discrete logic, etc.). For example, any or all of the components of the interfaces could be implemented by software, hardware, and/or firmware. Also, some or all of the machine readable instructions represented by the flowchart of FIG. 2 may be implemented manually. Further, although the example algorithm is described with reference to the flowcharts illustrated in FIG. 2, persons of ordinary skill in the art will readily appreciate that many other methods of implementing the example machine readable instructions may alternatively be used. For example, the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, or combined.

[0032] The API in the user device 118 may receive a query relating to a desired location such as an address (200). In this example, the request for noise data may be sent automatically from another information service that requires noise information in its presentation. An example of an information service is a real estate listing application. Another example may be a web-based application from a third-party server 122 shown in FIG. 1 that requests the noise information. For example, the web-based application from the third-party server 122 may be a real estate company site that accesses noise information via a query to one of the distributed servers 112 in FIG. 1. Alternatively, a human user may request noise data relating a specific address by inputting the address via a keypad or touchscreen on a user device. Of course other inputs may be used such as vocal input or graphic input from a graphical map displayed on the user computing device. Returning to FIG. 2, the application first determines whether the address is already in the database 114. If the address is already in the database 114, the noise evaluation data will be retrieved for the address (202). Data in the database 114 will be continuously updated, and the input data will be improved as more refined data becomes available. If the address is not in the database, the application will determine the address location for gathering relevant data from the data sources 102 (204). The relevant data is then grouped into different data groups such as the groups 104, 106, and 108 in FIG. 1.

[0033] The application then proceeds to evaluate different data from the different groups 104, 106, and 108 in parallel using distributed servers 112 in FIG. 1. Other groups of information may be established such as for public transportation, railroads, shipping, or frequent helicopter routes, for example. The application pulls road data, traffic data, elevation data, and building data from the region around the requested address to assemble the data group 104 for the selected region (206). In this example, the topography, road, and building data is pulled for the region extending between 500 meters to one kilometer from the points where the noise level is being estimated.

[0034] A 3-D model of a region of interest is built from the data group 104 (208). The model includes elevation contours, roads, and the best renderings of buildings that may be obtained from the data group 104. The buildings are often 3-D in urban areas, and then 2-D or simply parcel outlines when better data may not be obtained. 3-D outlines of buildings may be obtained from LIDAR data when it is available. In this example, traffic data is obtained from INRIX of Kirkland, Washington, and Kalibrate of Manchester, United Kingdom, and map data is obtained from HERE of Chicago, Illinois. Of course, similar data may be obtained from other public or private vendors. Initial processing of the data is performed before it is ready for use. In particular, data is often only available for a subset of significant roads or as measurements of traffic flow at specific points on roads. The information, therefore, must be extrapolated along the larger section of road.

[0035] The application computes a vehicle noise estimate in decibels from the data gathered (210). In one example, noise is estimated from vehicle traffic. The process begins with a 3-D map of a geographical area with roads, road traffic, topography, and 2D and 3D building outline data. Small lkm x 1km squares are extracted from the map and sent to a noise model processed by the servers 112. Of course, different sized squares may be extracted for this purpose. The noise model follows the very specific Federal Highway Authority's Traffic Noise Model 2.5 (TNM). In this example, following the Federal Highway Authority's TNM is required for performing noise studies for any municipality. The noise model calculates the noise created by vehicle traffic given the speed, volume, and types of vehicles (heavy trucks, light trucks, cars, buses, motorcycles). The model also takes into account the effects of the incline of the road and areas where deceleration and acceleration occur, such as at intersections. The frequency distribution and amplitudes for frequency bands is calculated, as these attenuate differently. The noise is then propagated through the environment, and reflections and refractions from the ground and buildings in the environment are calculated.

[0036] The application also pulls airport information from the airplane information group 106 in the region around the input address (212). The application determines if any airports are within ten kilometers of the point being evaluated. The application determines if the point is in a flight path, considers the number of flights per day from the airport and the types of aircraft, and estimates a score that corresponds to the Community Noise Equivalent Level (CNEL) using data science techniques based on airports where the CNEL has been determined and is publicly available. (214). The application also pulls all local point source information from the group of local data 108 (216). The local commercial noise value is computed as explained above and the most important sources are listed (218).

[0037] After receiving source data from the different sources, the application computes the noise assessment value (220). An example of the calculation of the aggregate noise level may be the following subroutine.

if wkday > 45

pointair = 0.6*point + air

else

pointair = point + air if wkday > 40 and pointair < 20

vehicle = 80 -2* (wkday - 40)

else

vehicle = 100-(wkday - 20)' scaling = max(l,2.5-(100-vehicle)/10) if wkday > 45

aggr. noise level = vehicle - pointair

else

aggr. noise level = vehicle -point-scaling* air

In this example, wkday is the estimated weekday, daytime vehicle noise level in decibels, point is the value assigned to local noise sources, and air is the value assigned to air traffic noise.

[0038] The value is then stored with the address in the database 114. The resulting data is returned to the user devices 118 from the database 114 (222) from either the calculated aggregate noise level value (220) or directly from the database 114 if the address had noise evaluation data already associated with it. For example, to compute noise estimates for the houses in a 1km by 1km square, each type of data is extracted for that square and then sent to the processing tool in the application. The processing tool executes all the steps prescribed by the Highway Authority's Traffic Noise Model for each building in the square or for a random subset of the buildings. When a random subset is calculated, the estimates for the remaining buildings are interpolated.

[0039] Another calculation for the aggregate noise level is to begin with a value of 100 everywhere. The weekday traffic noise estimate, and estimate for large airports, small airports, and local commercial noise are then computed. The final value is determined by Value = 100 - 0.65*vehicle traffic - 0.1*large airports - 0.1 small airports - 0.4 local commercial noise.

[0040] The results are loaded to the database 114 in the cloud 110, and the other types of noise that are gathered are added to the record associated with the address. All of these sources are combined and used to compute the aggregate noise level value. In this example, the aggregate noise level value is a range of 0-100, where the loudest sound levels are a score of 40 or less while the most peaceful areas are 90 or more. Each of the ranges is assigned a description. Thus, the "severe" range includes scores from 0-49, the "heavy" range includes scores from 50-59, the "moderate" range includes scores from 60-69, the "light" range includes scores from 70-79, the "quiet" range includes scores from 80-90, and the "peaceful" range includes scores from 91-100. Of course other ranges and descriptions may be used. For example, there may be three noise levels assigned. Soundscores below 68 are designated "Busy," Soundscores between 68 and 81 are designated "Active," and Soundscores above 81 are designated "Calm."

[0041] The application therefore determines an aggregate noise level value for the requested address. Another example of a system output may be a concise noise information package for individual buildings in the selected area. The system may also be implemented to provide noise estimates and information for every building in a county, a city, or other municipality. Another option may be to receive get more granular data such as separate noise level values on each side of a particular building or different noise level values for different floors in a building.

[0042] In this example, the application provides a uniform process for all areas of North America that takes in huge datasets. Human involvement in a computation is only at the very highest level. A user specifies the region for computation by either entering the corners of a polygon or by simply referring to an existing polygon that delineates the boundary of a municipality. Such polygons are available, for example, from HERE of Chicago, Illinois, or from state geographic information offices. Computation is dynamically distributed across cloud servers 112 in FIG. 1. The results of the noise evaluation are accessible on demand from user devices 118.

[0043] FIG. 3 is an image of a user interface 300. A user may display the current aggregate noise level value for an address on the user computing device. Other information may include vehicle noise average for a week, a weekend, day, or night. As may be seen in FIG. 3, the data may be included in a regional contour map. Also, houses and other buildings may be coded by color for noise. Noise from local sources such as stores, buses, or bars may be charted.

[0044] The user interface 300 may be generated on the user computing device for display of data computed by the applications on the servers 112 in the cloud 110 in FIG. 1. The user interface 300 includes an address summary field 302, a search field 304, a map area 306, a legend area 308, and an informational field 310. As explained above, the application allows the noise data to be displayed visually on the map area 306 and selected related data to be displayed in text on the informational field 310.

[0045] The address summary field 302 includes the address input by the user. The address search field 304 allows the user to input another address for noise evaluation. As explained above, the application on the servers 112 will return sound data relating to input address in the form of the user interface 300.

[0046] The map area 306 includes a 2D map in this example of the region around the requested address. The map area 306 may also include an option for the user to view the region in three dimensions. The map area 306 may include zoom in and zoom out controls to change the scale of the map. The user may also reposition the map area 306 with different input devices such as a touch screen or a mouse associated with the user computing device. The map area 306 includes outlines of buildings 322 and a specific outline 324 of the building matching the input address. Different areas are shaded in different colors according to the estimated decibel level of that area due to vehicle traffic. For example, certain regions are quiet such as a region 326 and therefore are presented in green shades in this example. Certain regions are moderately noisy such as a region 328 and are presented in yellow shades. Certain regions are noisy such as a region 330 and are presented in red or orange shades. For example, the region 330 is near a busy street and therefore accounts for the high noise level.

[0047] The legend area 308 includes a color key with different colors on the map area 306 representing different ranges of aggregate noise level values. In this example, there are six levels of noise ranging from quiet, occasional, light, moderate, heavy, and severe. In this example, the colors correspond to ranges of decibel readings between 58 and higher than 80, where the higher decibel readings indicate louder sounds. Of course other ranges and differing numbers of levels may be used.

[0048] The informational field 310 includes text formatted noise data relating to the region that lists significant sources of noise close to the building of interest. These sources may be automotive maintenance businesses, schools, bars, etc. For example, a specific aggregate noise level value 340 may be displayed. In this example, the specific aggregate noise level value of 66 is shown. The information field includes a commercial noise field 342. The commercial noise field 342 indicates the level of noise from commercial establishments in proximity to the selected building. In this example, the commercial noise is shown as "high." The rating is also in a red color in this example to correspond with the level of the commercial noise relative to the scale in the map legend 308. A transportation summary field 344 may be included to indicate information on transportation infrastructure in proximity to the address. For example, information on air or rail traffic may be included. Information relating to the nearest roads to the selected address may also be included. A list of noise sources 346 that may generate noise in proximity of the selected address are also listed in the informational field 310. In this example, the local noise sources from different establishments such as a fire station, an auto parts business, and a hotel are listed. The listed sources may be selected and thereby be displayed on the map area 306 for reference.

[0049] The selected address may be shown in a photographic map by selecting the specific outline 324 of the building in FIG. 3. FIG. 4A is an example interface 400 that may be displayed on the user computing device showing a photographic map 402 that includes a noise scale 404 and a specialized circular icon 406 on the building matching the address selected from the interface 300 in FIG. 3. The noise scale 404 includes different ranges of aggregate noise level values determined by the application for structures such as buildings in the surrounding area from the selected address. The different ranges of aggregate noise level values are indicated by colors for assisting a user to determine the level of noise of a particular structure. In this example, the loudest areas have red or orange colors, medium noise has a yellow color, and quieter areas have shades of green similar to the scale in FIG. 3. The photographic map 402 includes noise designation icons 410 in the form of colored dots in this example. Each of the noise designation icons 410 are associated with a building or structure shown in the photographic map 402. The color of the dots 410 corresponds with different aggregate noise level values for the building based on the sound scale 404.

[0050] The photographic map 402 also includes an informational field 420 for displaying other relevant data to the location. In this example, the information field 420 includes an aggregate noise level value icon 422 showing the aggregate noise level value for the selected address. The aggregate noise level value icon 422 is in the same color as the scale in the sound scale 404. In this example, the aggregate noise level value icon 422 shows a value of "58" and is presented in orange, indicating heavy noise. The information field 420 also includes a listing 424 that rates local commercial noise. The rating may be in the color code of the scale 404 on the map. In this example, the rating 424 is in yellow, indicating moderate noise. The information field 420 also includes an airport listing 426 that includes any airports within a certain distance (e.g., six miles) of the selected address. The information field 420 also includes a local noise source listing 428 that includes any local sources of potential noise such as commercial establishments. The local sources of noise may be shown on the photographic map 402 in proximity to the selected address. Similar to the map area 306 in FIG. 3, the user may zoom in or out of the area via a scale slider 430.

[0051] FIG. 4B is another example of an image 450 showing a selected building or address from the user interface image 300 in FIG. 3. Similar to the image 400, the image 450 shows a photographic map 452 of the selected building with a marker icon 454 and icons with color coding as to the aggregate noise level values of the surrounding buildings. The image 450 also includes an information field 456 for text formatted output of noise data from surrounding noise sources. In this example, the aggregate noise level value is a 59 and therefore is colored in orange indicating that the noise is heavy.

[0052] FIG. 5A is another example interface 500 that may be displayed on the user computing device. The interface 500 in FIG. 5A includes a pop-up screen 502 that is overlaid on a separate website display 504. The pop-up screen 502 includes requested noise data that is populated with data from the database 114 in FIG. 1. For example, the website display 504 relates to a real estate server with information about a property. The real estate company web-server such as the server 122 in FIG. 1 provides the functionality of providing noise data for the selected property that is obtained from the database 114.

[0053] The pop-up screen 502 that includes a map area 510, a legend area 512, and an informational field 514. A specialized icon 520 is shown on the map area 510 matching the address selected from the website display 504. As explained above, the application allows the noise data to be displayed visually on the map area 510 and selected related data to be displayed in text on the informational field 514.

[0054] The map area 510 includes a 2D map in this example of the region around the requested address. The map area 510 may also include an option for the user to view the region in three dimensions. The map area 510 may include a zoom in and zoom out control 522 to change the scale of the map. The user may also reposition the map area 510 via a position control 524 with different input devices such as a touch screen or a mouse associated with the user computing device. Different areas are shaded in different colors according to the estimated noise level of that area due to vehicle traffic, airport noise, and commercial noise. For example, certain areas are quiet and therefore are presented in green shades in this example. Certain areas are moderately noisy and are presented in yellow shades. Certain areas are noisy and are presented in red or orange shades.

[0055] The legend area 512 includes a color key with different colors on the map area 510 representing different ranges of aggregate noise level values. In this example, there are three levels of noise ranging from calm to active to busy. In this example, the colors correspond to ranges of sound scores between 100 (quietest) and 50 (loudest). Of course other ranges and differing numbers of levels may be used. The different ranges of aggregate noise level values are indicated by colors for assisting a user to determine the level of noise of a particular structure. In this example, the loudest areas have red or orange colors, medium noise has a yellow color, and quieter areas have shades of green. In this example, scores below 68 are designated "busy," scores between 68 and 81 are designated "active," and scores above 81 are designated "calm." The ranges and descriptions of the ranges may be defined by the user or the application.

[0056] The informational field 514 includes text formatted noise data relating to the selected location. For example, a specific aggregate noise level value 530 may be displayed. In this example, the specific aggregate noise level value of 72 is shown as well as the designation of "active." The information field includes a commercial noise field 532. The commercial noise field 532 indicates the level of noise from commercial establishments in proximity to the selected location. In this example, the commercial noise is shown as "active." An airport noise field 534 indicates the level of noise from airports in proximity to the selected location. In this example, the airport noise is shown as "calm." A traffic summary field 536 may be included to indicate information on traffic in proximity to the address. In this example, the traffic noise is shown as "busy." In this example, the description of the each information field 532, 534, and 536 may be changed by the user or the application.

[0057] FIG. 5B is another example of an interface 550 similar to that in FIG. 5A. The interface 550 is standalone for display of noise information and may be displayed on a user device. The interface 550 includes a map area 560, a legend area 562, and an informational field 564 similar to those in FIG. 5A. A specialized icon 570 on the map area 560 matches the location selected by a user. As explained above, the application allows the noise data to be displayed visually on the map area 560 and selected related data to be displayed in text on the informational field 564.

[0058] The map area 560 includes a 2D map in this example of the region around the requested address. The map area 560 may include a zoom in and zoom out control 572 to change the scale of the map. The user may also reposition the map area 560 via a position control 574. Different areas are shaded in different colors according to the estimated noise level of that area due to vehicle traffic, airport noise, and commercial noise. For example certain areas are quiet and therefore are presented in green shades in this example. Certain areas are moderately noisy and are presented in yellow shades. Certain areas are noisy and are presented in red or orange shades. For example, roads may be shaded in red or orange due to the heightened noise from traffic.

[0059] The legend area 562 includes a color key with different colors on the map area 560 representing different ranges of aggregate noise level values. In this example, similar to FIG. 5A, there are three levels of noise ranging from calm to active to busy. The different ranges of aggregate noise level values are indicated by colors for assisting a user to determine the level of noise of a particular structure. In this example, the loudest areas have red or orange colors, medium noise has a yellow color, and quieter areas have shades of green. In this example, scores below 68 are designated "busy," scores between 68 and 81 are designated "active," and scores above 81 are designated "calm."

[0060] The informational field 564 includes text formatted noise data relating to the selected location. For example, a specific aggregate noise level value 580 may be displayed. In this example, the specific aggregate noise level value of 72 is shown as well as the designation of "active." The information field includes a local sources noise field 582. The local sources noise field 582 indicates the level of noise from sources in proximity to the selected location. In this example, the local noise sources field 582 is shown as "active." An airports noise field 584 indicates the level of noise from airports in proximity to the selected location. In this example, the airports noise is shown as "calm." A traffic field 586 may be included to indicate information on traffic in proximity to the address. In this example, the traffic noise is shown as "busy."

[0061] As explained above, the system in FIG. 1 may also be used to generate sound maps independent of a request for generation of sound information for a certain location. Thus, another application that may be run by the servers 112 is an application for the pre-generation of sound maps for an area in FIG. 1. Such maps may then be stored in a storage device such as the results database 114 and be used for returning requested sound data to a requesting user device or other device. For example, sound maps of the 1,000 largest urban regions in the United States may be determined in advance and made available to the user devices 118 or third-party server 122 in FIG. 1.

[0062] FIG. 6 is a flow diagram of the process of producing a pre-generated sound map for a certain region. The flow diagram in FIG. 6 is representative of example machine readable instructions for gathering noise-related data and producing a noise map for a certain region by the noise evaluation system 100 in FIG. 1. In this example, the machine readable instructions comprise an algorithm for execution by: (a) a processor, (b) a controller, and/or (c) one or more other suitable processing device(s). The algorithm may be embodied in software stored on tangible media such as, for example, a flash memory, a CD-ROM, a floppy disk, a hard drive, a digital video (versatile) disk (DVD), or other memory devices, but persons of ordinary skill in the art will readily appreciate that the entire algorithm and/or parts thereof could alternatively be executed by a device other than a processor and/or embodied in firmware or dedicated hardware in a well-known manner (e.g., it may be implemented by an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable logic device (FPLD), a field programmable gate array (FPGA), discrete logic, etc.). For example, any or all of the components of the interfaces could be implemented by software, hardware, and/or firmware. Also, some or all of the machine readable instructions represented by the flowchart of FIG. 6 may be implemented manually. Further, although the example algorithm is described with reference to the flowcharts illustrated in FIG. 6, persons of ordinary skill in the art will readily appreciate that many other methods of implementing the example machine readable instructions may alternatively be used. For example, the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, or combined.

[0063] The application first divides the entire world into a grid of squares having a pre- determined dimension (600). In this example, each of the squares is approximately 40 by 40 meters or .0004 angular degrees by .0004 angular degrees. A set of sub-regions is selected by the user and input to the application (602). In this example, the sub-regions may be an urban region or a set of counties. The squares for the selected sub-regions are assigned to a map of the region (604).

[0064] The application then creates unique databases in "layer" or raster format for each type of noise source considered for the selected regions. In this example, there are three types of noise source databases, local point sources, vehicle traffic sources, and airport sources. Of course other types of sources such as rail, commuter rail, public transit, etc. may be added. The application computes a density map for local noise sources (e.g., stores, schools, restaurants, etc.) for the selected region (606). The data for the local noise sources may be taken from the local sources data group 108 of the data sources 102 in FIG. 1. One database or layer is created for point source information that assigns a value for local noise sources to each grid square in the region (608).

[0065] The application then places vehicle traffic estimate points in each grid square in the selected region (610). The application then evaluates the noise level at each of these points and averages the points in one grid to determine a value for that grid square (612). A simple average of traffic values or other forms of averaging may be used. The data for the vehicle traffic estimates may be taken from the vehicle traffic data group 104 of the data sources 102 in FIG. 1. The application then creates a database or layer for vehicle traffic (614).

[0066] The application then analyzes each airport in the selected region by creating noise contours around the airport using publicly available flight information (616). The data for the airport analysis may be taken from the airplane data group 106 of the data sources 102 in FIG. 1.

[0067] The application then creates a score layer or database, which is created by multiplying each layer (local, traffic, and airport) by a weight (618). The layers are summed or aggregated and then this value is subtracted from 100 in this example. The output of the value for a pre-determined number of locations such as for each of the square in the region is stored as a sound map for the region (620). Thus, one raster format is created for each layer or type of input and one score layer is created for the region. These are all set on the same grid structure, which means that they may be worked with easily, such as adding values from multiple layers.

[0068] As explained above, when a user device sends a query for a location in one of the selected regions, the application on the servers 112 may perform a very fast look up from the grid structure of a pre-generated map of a region and return the value from each of the three component layers and from the score layer. As explained above, each of the scores may be translated into an adjective (e.g., "calm, active, busy"). The score layer may be presented as a visual map as shown in FIGs. 3- 5 and may be included on third-party websites. The values may be color-coded as explained above. The application may include tools to select all the squares in any region, and then the application may recalculate the values of any individual layer for each of those squares, and then of course update the score layer. The application may also create a new region and only compute those squares that are not yet calculated within the new region.

[0069] It is to be understood that the maps generated in the example interfaces shown in FIGs 3, 4, and 5 may be generated prior to user query of a particular location and stored in the databases 114 in FIG. 1. When a particular location is requested, the application accesses the previously created maps to create the display.

[0070] FIG. 7 is a large scale interface screen 700 of a sound map of a large area. The sound map shown in FIG. 7 may either be generated in advance and stored or generated in response to a query for sound data around a particular location. The interface screen 700 includes a map area 710, a legend area 712, and a layer control 716. A specialized icon 720 may be displayed on the map area 710 matching a requested location. As explained above, the application allows the noise data to be displayed visually on the map area 710.

[0071] The map area 710 includes a 2D map in this example of the region around the requested address. The map area 710 may also include an option for the user to view the region in three dimensions. The map area 710 may include a zoom in and zoom out control 722 to change the scale of the map. The user may also reposition the map area 710 via a position control 724 with different input devices such as a touch screen or a mouse associated with the user computing device. Different areas are shaded in different colors according to the estimated noise level of that area due to vehicle traffic, airport noise, and commercial noise. For example, certain areas are quiet and therefore are presented in green shades in this example. Certain areas are moderately noisy and are presented in yellow shades. Certain areas are noisy and are presented in red or orange shades.

[0072] The legend area 712 includes a color key with different colors on the map area 710 representing different ranges of aggregate noise level values. In this example, there are three levels of noise ranging from calm, active, and busy. In this example, the colors correspond to ranges of sound scores between 100 (quietest) and 50 (loudest). Of course other ranges and differing numbers of levels may be used. The different ranges of aggregate noise level values are indicated by colors for assisting a user to determine the level of noise of a particular location. In this example, the loudest areas have red or orange colors, medium noise has a yellow color, and quieter areas have shades of green. In this example, scores below 68 are designated "busy," scores between 68 and 81 are designated "active," and scores above 81 are designated "calm." The ranges and descriptions of the ranges may be defined by the user or the application.

[0073] The layer control 716 allows a user to add or subtract layers to the map as well as overlay graphical information. In this example, the layer control 716 allows a user to toggle the location icon 720. The user may also toggle the sound score on or off. The user may select one of three base layers in this example. The base layers may include a Google street view, a photographic satellite Google view, or an Open StreetMap view. In this example, the photographic satellite view is selected.

[0074] The noise evaluation system 100 therefore provides on-demand immediate computation of the noise data for a particular location. For example, the noise evaluation system 100 can calculate a vehicle noise decibel estimate and aggregate the aggregate noise level value at any point within several seconds. When a user queries the API for an address that is not already in the database 114, the entire process runs in several seconds and the data is returned to the user.

[0075] The noise evaluation system 100 is not based on pre-partitioning of any geographic area for which an aggregate noise level value may be determined by the noise evaluation system 100. Relatively large databases 114 on the order of a terabyte are used and pull from these regions to compute the data as needed. The benefit is that there is no database redundancy and the partition is optimal for the computation at hand. If data is kept in a partitioned structure, such as many small regions, then the database inherently has a more complicated structure and maintaining many small regions requires much more work. As an example, the buildings file for a one hundred square kilometer urban region is at least one gigabyte, and a city such as Los Angeles is several thousand square kilometers. Maintaining hundreds or thousands of small files would be very resource intensive. The application, however, keeps very large files encompassing entire states and several terabytes in size. Small regions are extracted only when needed, and after they are used for computation they are discarded.

[0076] The noise evaluation system 100 provides complete automation of large scale computations for such evaluations. The system 100 specifies a region such as a county by a series of points on its perimeter, and it is partitioned into small squares. The squares are then processed by calculating the vehicle noise decibel estimate of each building or a random subset. When a random subset is calculated, the estimates for the remaining buildings are interpolated.

[0077] Human input time is independent of computation size. The human time involved in running a computation is minimal, only the boundary needs to be specified. Generally, the database 114 has polygons for the boundary, such as a city, county, or state, and in this case the application only specifies the polygon. The application then partitions the polygon into smaller regions and extracts the necessary data from the large databases iteratively for each small region. Most significantly, the amount of human time necessary to run a computation is independent of the size of the region or polygon. Thus a computation for every building in a small town or an entire state can be performed in the same amount of human time. Distributed computing coordinates the computation over multiple servers 112, each with many central processing units. Using cloud services, an arbitrary number of servers 112 can be activated, and by distributing the computational work for each small subregion among these servers 112, the time necessary to compute an entire large region can be made very short.

[0078] Adaptive computation is also employed by the noise evaluation system 100. The noise evaluation system 100 adapts to the size of the computation. When a job is very large and there are many small squares to process, more servers are turned on and the jobs are distributed among the servers 112 in the cloud. [0079] An example computer system 800 in FIG. 8 includes a processor 802 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both), a main memory 804, and a static memory 806, which communicate with each other via a bus 808. The computer system 800 may further include a video display unit 810 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system 800 also includes an input device 812 (e.g., a keyboard), a cursor control device 814 (e.g., a mouse), a disk drive unit 816, a signal generation device 818 (e.g., a speaker), and a network interface device 820.

[0080] The disk drive unit 816 includes a machine- readable medium 822 on which is stored one or more sets of instructions (e.g., software 824) embodying any one or more of the methodologies or functions described herein. The instructions 824 may also reside, completely or at least partially, within the main memory 804, the static memory 806, and/or within the processor 802 during execution thereof by the computer system 800. The main memory 804 and the processor 802 also may constitute machine-readable media. The instructions 824 may further be transmitted or received over a network.

[0081] While the machine-readable medium is shown in an example to be a single medium, the term "machine-readable medium" should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term "machine-readable medium" can also be taken to include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the various embodiments, or that is capable of storing, encoding, or carrying data structures utilized by or associated with such a set of instructions. The term "machine-readable medium" can accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.

[0082] A variety of different types of memory storage devices, such as a random access memory (RAM) or a read only memory (ROM) in the system or a floppy disk, hard disk, CD ROM, DVD ROM, or other computer readable medium that is read from and/or written to by a magnetic, optical, or other reading and/or writing system that is coupled to the processor, may be used for the memory.

[0083] Various embodiments of the invention are described above in the Detailed Description. While these descriptions directly describe the above embodiments, it is understood that those skilled in the art may conceive modifications and/or variations to the specific embodiments shown and described herein. Any such modifications or variations that fall within the purview of this description are intended to be included therein as well. Unless specifically noted, it is the intention of the inventors that the words and phrases in the specification and claims be given the ordinary and accustomed meanings to those of ordinary skill in the applicable art(s).

[0084] The foregoing description of various examples known to the applicant at this time of filing the application has been presented and is intended for the purposes of illustration and description. The present description is not intended to be exhaustive nor limit the invention to the precise form disclosed and many modifications and variations are possible in the light of the above teachings. The examples described serve to explain the principles of the invention and its practical application and to enable others skilled in the art to utilize the invention in various examples and with various modifications as are suited to the particular use contemplated. Therefore, it is intended that the invention not be limited to the particular embodiments disclosed for carrying out the invention.

[0085] While particular examples of the present invention have been shown and described, it will be obvious to those skilled in the art that, based upon the teachings herein, changes and modifications may be made without departing from this invention and its broader aspects. It will be understood by those within the art that, in general, terms used herein are generally intended as "open" terms (e.g., the term "including" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "includes" should be interpreted as "includes but is not limited to," etc.).

Claims

WHAT IS CLAIMED IS:

1. A system for performing sound evaluation of a location, the system comprising:

a database interface coupleable to a plurality of geographic and other databases;

a user interface device including an input device to accept a request of noise data relating to a location;

a controller to aggregate relevant geographic and noise data in proximity to the requested location from the database interface, the controller calculating an aggregate sound level value for the requested location and returning the aggregate sound level value to the user interface device.

2. The system of claim 1 , wherein the controller constructs a sound map of the area around the requested location, the sound map including data relating to the sound level value in locations of the area around the requested location.

3. The system of claim 2, wherein the controller returns the map data for display on the user interface device.

4. The system of claim 3, wherein the noise data is displayed visually on a map area on the user interface device.

5. The system of claim 3, wherein the noise data is in a sound score generated from at least one of traffic noise data, commercial building noise data and airport data.

6. The system of claim 5, wherein the noise data is displayed in text form.

7. The system of claim 5, wherein the noise data is visually classified on the map by different colors relating to different ranges of scores relating to the noise data.

8. A system to build a sound data map of a geographic region, the system comprising:

a database interface coupleable to a plurality of databases including at least one geographic database and at least one database having noise data; a controller to aggregate noise data in proximity to a predetermined number of locations within the geographic region from the database interface, the controller generating a sound data map for the geographic region; and

a storage device for storing the generated sound data map for the geographic region.

9. The system of claim 8, wherein a user interface device sends a query for a location within the geographic region and wherein the controller generates sound evaluation data for the location from the sound data map for the geographic region, the user interface device displaying the sound evaluation data.

10. The system of claim 8, wherein a user interface device sends a query for a location, and wherein the controller aggregates noise data in response to receiving the query for a location.

11. The system of claim 8, wherein the sound data map is stored in a storage device and wherein the sound data map is available to a website

12. The system of claim 11, wherein the website is a real estate website.

13. The system of claim 8, wherein the noise data is displayed visually on a map area.

14. The system of claim 8, wherein the noise data is in a sound score generated from at least one of traffic noise data, commercial building noise data and airport data.

15. The system of claim 14, wherein the noise data is displayed in text form.

16. The system of claim 14, wherein the noise data is visually classified on the map by different colors relating to different ranges of scores relating to the noise data.

17. A method to build a sound data map for a selected region, the method comprising:

dividing an area into squares having a pre-determined dimension;

selecting a region within the area; determining a local noise score, a traffic noise score and an airport noise score for each of the squares within the selected region;

determining a sound score overlay for the region; and

storing the sound score overlay for the region in the form of a sound data map.

18. The method of claim 17, further comprising displaying the sound data map on a display device.

19. The method of claim 17, further comprising:

accepting a query for noise data for a location within the region from a user device; and determining a sound score for the location based on the sound score overlay.