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Any android application for field mapping with Google map in background

Any android application for field mapping with Google map in background


I need a android/iPhone app, through which I can draw point/line/polygon while survey only. The app should have facility to use Google satellite imagery or provision for uploading base maps. Please suggest, if any.


I think "My Maps" by Google does exactly this. You can draw lines, areas, points, pictures, etc. It also seems that you can customize the look and share them. The same functionality is also available in a browser, by clicking (menu icon)-->"Your places"-->"MAPS" in google maps.

I have used it in the past for planning out parking, etc. for a large outdoor party, and I found it quite suitable.


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Novosibirsk, Novosibirsk, Russian Federation

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Ke Chankrisna

Siem Reap, Siem Reap, Cambodia

Android App development.

7 years of professional experience in Android app development. Parking app, Bluetooth app, Uber app, Chatting app, Geolocation app, Image processing app, Social app, Fitness app, Browser app, Shopping app User-friendly design, Bug-free, Smart working, High speed. Full-time availability during the contract, reliable communication. View more

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Singapore, Singapore, Singapore

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Longville Park Free Town P.A., Clarendon, Jamaica

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Rawalpindi, Punjab, Pakistan

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Mapping Google Drive as Network Drive without syncing

I'm trying to learn if there's any way to map Google Drive on a client Windows workstation as a network drive without actually having to sync any files.

We have a NAS on-site that syncs with Google Drive, so what we're hoping to do is allow access to Google Drive when employees are working remotely, thereby removing the need for a VPN.

I know how to install Google Drive and selectively sync folders, but that's not a viable option, as all of the folders need to show up on the employees workstation so that they can easily browse / copy / save files in those folders.


Handling Errors

Below is a table containing all of the gRPC error codes defined in google.rpc.Code and a short description of their cause. To handle an error, you can check the description for the returned status code and modify your call accordingly.

HTTP gRPC Description
200 OK No error.
400 INVALID_ARGUMENT Client specified an invalid argument. Check error message and error details for more information.
400 FAILED_PRECONDITION Request can not be executed in the current system state, such as deleting a non-empty directory.
400 OUT_OF_RANGE Client specified an invalid range.
401 UNAUTHENTICATED Request not authenticated due to missing, invalid, or expired OAuth token.
403 PERMISSION_DENIED Client does not have sufficient permission. This can happen because the OAuth token does not have the right scopes, the client doesn't have permission, or the API has not been enabled.
404 NOT_FOUND A specified resource is not found.
409 ABORTED Concurrency conflict, such as read-modify-write conflict.
409 ALREADY_EXISTS The resource that a client tried to create already exists.
429 RESOURCE_EXHAUSTED Either out of resource quota or reaching rate limiting. The client should look for google.rpc.QuotaFailure error detail for more information.
499 CANCELLED Request cancelled by the client.
500 DATA_LOSS Unrecoverable data loss or data corruption. The client should report the error to the user.
500 UNKNOWN Unknown server error. Typically a server bug.
500 INTERNAL Internal server error. Typically a server bug.
501 NOT_IMPLEMENTED API method not implemented by the server.
502 N/A Network error occurred before reaching the server. Typically a network outage or misconfiguration.
503 UNAVAILABLE Service unavailable. Typically the server is down.
504 DEADLINE_EXCEEDED Request deadline exceeded. This will happen only if the caller sets a deadline that is shorter than the method's default deadline (i.e. requested deadline is not enough for the server to process the request) and the request did not finish within the deadline.
Warning: Google APIs may concurrently check multiple preconditions for an API request. Returning one error code does not imply other preconditions are satisfied. Application code must not depend on the ordering of precondition checks.

Retrying Errors

Clients may retry on 503 UNAVAILABLE errors with exponential backoff. The minimum delay should be 1s unless it is documented otherwise. The default retry repetition should be once unless it is documented otherwise.

For 429 RESOURCE_EXHAUSTED errors, the client may retry at the higher level with minimum 30s delay. Such retries are only useful for long running background jobs.

For all other errors, retry may not be applicable. First ensure your request is idempotent, and see google.rpc.RetryInfo for guidance.

Propagating Errors

If your API service depends on other services, you should not blindly propagate errors from those services to your clients. When translating errors, we suggest the following:

  • Hide implementation details and confidential information.
  • Adjust the party responsible for the error. For example, a server that receives an INVALID_ARGUMENT error from another service should propagate an INTERNAL to its own caller.

Reproducing Errors

If you cannot resolve errors through analysis of logs and monitoring, you should try to reproduce the errors with a simple and repeatable test. You can use the test to collect more information for troubleshooting, which you can provide when contacting technical support.

We recommend you use oauth2l and curl -v and System Parameters to reproduce errors with Google APIs. Together they can reproduce almost all Google API requests, and provide you verbose debug information. For more information, see the respective documentation pages for the API you are calling.


The image itself, but universal search bar in the data can now be a richer browsing history

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Any android application for field mapping with Google map in background - Geographic Information Systems

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A Brief History of Crime Mapping

The history of crime mapping is punctuated by three defining moments. Ever since the first recognizable examples of crime maps were produced in the early nineteenth century by André-Michel Guerry and Adolphe Quetelet, following the publication in France of the first national crime statistics, cartography has maintained something of a flickering presence in criminology ( Courtright and Mutchnick 2002). In many ways, Guerry and Quetelet’s cartes thematiques anticipated contemporary crime mapping, with areas shown shaded according to the rates of various categories of crime, as well as other socio-demographic features, such as poverty and education levels ( Beirne 1993 Wortley and Mazerolle 2008 see Figure 1). Importantly, Guerry and Quetelet’s maps showed that crimes were not evenly distributed but clustered geographically along with other observable social variables such as poverty and education levels, and that such patterns were consistent over time ( Courtright and Mutchnick 2002 Wortley and Mazerolle 2008 Hill and Paynich 2013). Credited with founding what is now termed the ‘cartographic school’, Guerry and Quetelet’s methods formed the basis of a flourish of studies throughout the nineteenth century—perhaps the most well-known of which is Mayhew’s (1862) survey of London’s rookeries—as well as offering a direct challenge to Lombroso’s biological positivism ( Chainey and Ratcliffe 2005).

Adriano Balbi and André Michel Guerry, 1829, Statistique comparée de [l’é]tat de l’instruction et du nombre des crimes [d]ans les divers arrondissemens des Académies et des Cours Rles de France.

Adriano Balbi and André Michel Guerry, 1829, Statistique comparée de [l’é]tat de l’instruction et du nombre des crimes [d]ans les divers arrondissemens des Académies et des Cours Rles de France.

The next substantial and somewhat more influential moment in the history of crime mapping, to which modern criminology’s fascination with charting the spatial distribution of crime can be more or less directly traced, is the work of the Chicago School sociologists in the 1920s and 1930s. The theoretical and empirical initiatives of the Chicago School and its legacy have been discussed at length elsewhere and need not be reiterated here (see, e.g. Hayward 2013). However, of particular relevance to the present discussion is the iconic significance of Ernest Burgess’s ‘concentric zone’ model ( Park et al. 1925), arguably ‘the most famous diagram in social science’ ( Davis 1995). Inherent in Burgess’s model is a particular way of seeing urban space which has endured for almost a century (see Hayward 2004): today, the concentric zone model remains the quintessential expression of criminology’s conception of the city. 1 Schematic, rationalized and ‘mappable from above’ ( Hayward 2004: 97), the model is emblematic of what the French philosopher and urban theorist, Michel de Certeau (1984), would later describe as the ‘concept city’: ‘. the city as seen by planners, developers, statisticians and, all too often, criminologists . distilled to leave only quantitative data, demographics and rational discourse’ ( Hayward 2004: 2). Unfortunately, it is the School’s superficial interpretation of urban space which has since predominated within criminology, having ‘set the geography of crime down a … narrow conceptual path from which it has rarely deviated’ ( Hayward 2012: 443).

The next significant development in crime mapping occurred during the late 1970s and 1980s when the so-called ‘second wave’ of environmental criminology reinvigorated interest in the spatial distribution of crime ( Hayward 2004). In the following decade, developments in computerized mapping software, the availability of affordable hardware and the digitization of police records heralded a new era of ‘desktop’ crime mapping ( Chainey 2009). Today, digital crime mapping software is used by police forces around the world to identify crime ‘hot spots’ and to target resources where they are apparently most needed (see, e.g. the volume edited by Chainey and Tompson 2008). Recent developments include acoustic gunshot detection systems, which use hidden microphones and sensors to triangulate and alert the police to the location of gunshots in real time, and predictive crime mapping software, which enables police to identify ‘prospective hot spots’ ( Bowers et al. 2004 see www.predpol.com). Perhaps most significantly, however, recent years have seen a growing trend in the publication of crime maps on the internet, often hosted on the websites of police departments, and increasingly in an interactive format ( Ratcliffe 2002 see, e.g. www.police.uk or maps.met.police.uk).


2D to 3D geologic mapping transformation using virtual globes and flight simulators and their applications in the analysis of geodiversity in natural areas

This work describes the transformation process from 2D cartography to 3D, simply by overlapping images in common formats (jpeg, bmp, tiff, png, etc.) on Google Earth’s virtual globe. Arribes del Duero Natural Park, located west of the province of Salamanca, Spain, was the object of this part of the study. Other natural areas are also discussed and were used to establish a procedure for mapping geodiversity and for identifying areas of geological uniqueness and naturalness within the natural areas. To do this, different parametric indices were used to empirically generate different degrees of geological diversity in the Quilamas Natural Area, located south of Salamanca, Spain. Intermediate parametric maps were processed using two types of GIS technical: graphical (neighbourhood operations) and alphanumeric (calculated from the fields in the attribute table). Intermediate parametric maps were processed using two types of technical GIS: neighbourhood operations (graphics) and alphanumeric (calculated from the fields in the attribute table). These maps were used to establish areas with the greatest concentration of geological diversity elements and to define areas with a greater need for protection when planning the management of human activities in natural areas. Finally, the flight simulator tool, which was implemented in the free virtual globe and controlled using a keyboard or joystick, allows you to “fly” through the projected geological mapping of Arribes Del Duero Natural Park or view the parametric mapping and geodiversity in the newly created Quilamas Natural Area. Interoperability with the Google Maps application allows you to identify and observe the outcrops of the various geological materials in natural or anthropic terrain cuts.

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Baidu Maps is a desktop and mobile web mapping service application and technology provided by Baidu, offering satellite imagery, street maps, street view and indoor view perspectives, as well as functions such as a route planner for traveling by foot, car, or with public transportation. Android and iOS applications are available. Baidu Maps is available only in the Chinese language and before 2016, it offered maps only of mainland China, Hong Kong, Macau and Taiwan, the rest of the world appearing unexplored. Currently Baidu Maps also offers maps of various other countries. It was reported .

The Bdote Memory Map is an interactive online map and website by which the Dakota people trace their history and passed down memory of the region that is now referred to as the Twin Cities area in Minnesota. It allows viewers to explore the history of the region as told from an indigenous perspective. With video, audio, and other multimedia forms, the map provides the voices of Native speakers to tell their personal and ancestral stories of the region at the junction of the Mississippi and Minnesota Rivers, known by the Dakota people as Bdote, meaning "where the two waters come together".


Street map overlays, in some areas, may not match up precisely with the corresponding satellite images. The street data may be entirely erroneous, or simply out of date:

The biggest challenge is the currency of data, the authenticity of data," said Google Earth representative Brian McLendon. In other words: The main complaints the Google guys get are "that's not my house" and "that's not my car." Google Maps satellite images are not in real time they are several years old. [41]

Restrictions have been placed on Google Maps through the apparent censoring of locations deemed potential security threats. In some cases the area of redaction is for specific buildings, but in other cases, such as Washington, D.C., [43] the restriction is to use outdated imagery. These locations are fully listed on Satellite map images with missing or unclear data.

Google Maps has difficulty processing road data when dealing with cross-boundary situations. For example, users are unable to obtain a route from Hong Kong to Shenzhen via Shatoujiao, because Google Maps does not display and plan the road map of two overlapping places. [44]

Sometimes objects on Google Maps are hidden by clouds. For example, the mast of Arbrå Transmitter near Bollnäs in Sweden is hidden under a cloud.

The map uses the Mercator projection, which is increasingly distorted towards the polar regions.

Sometimes the names of geographical locations are inaccurate and Google is unable (or unwilling) to correct the data. An example of this type of error may be found in Google Maps Laona, Wisconsin. In this instance Google Maps identifies one of the town's two major lakes as "Dawson Lake" [45]   the USGS, State of Wisconsin, and local government maps all identify that map feature as "Scattered Rice Lake". [46]

Google collates business listings from multiple on-line and off-line sources. To reduce duplication in the index, Google's algorithm combines listings automatically based on address, phone number, or geocode. [47] This merges single listings into one record combining aspects of both listings. [48]


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