Claims:1. A system (100) for urban planning, comprising:
a processing module (102) comprising a plurality of hardware processors;
one or more Input/Output (I/O) interfaces (103); and
a memory module (101) comprising a plurality of instructions, said plurality of instructions when executed cause at least one of the plurality of hardware processors to:
collect (202) data pertaining to a current city model;
collect (204) at least one real-time input pertaining to an optimization requirement for the current city model;
analyzing (206) the data pertaining to the current city model based on the at least one real-time input and one or more reference city models from a reference database; and
generating (208) at least one recommendation in response to the optimization requirement collected, based on the analysis of the data pertaining to the current city model, the at least one input pertaining to the optimization requirement, and the one or more reference city models, wherein the at least one recommendation comprises of at least one optimization scenario.
2. The system as claimed in claim 1, wherein the system (100) generates the at least one recommendation by:
extracting (302) a plurality of characteristics from the optimization requirement;
extracting (304) a plurality of characteristics from the one or more reference city models;
identifying (306) a plurality of matches between the plurality of characteristics from the optimization requirement and the plurality of characteristics from the one or more reference city models;
listing (308) characteristics corresponding to said plurality of matches in the order of relevance, wherein the relevance is measured in terms of extent of similarity between the plurality of characteristics of the optimization requirement and the plurality of characteristics of the one or more city models;
selecting (310) a specific number of most relevant characteristics as close matches, from the characteristics listed in the order of relevance;
computing (312) a distance in characteristics value (di) for each of the most relevant characteristics;
identifying (314) one or more characteristics as characteristics to be optimized, based on corresponding di score;
generating (316) at least one improvement program for optimization of each of the characteristics identified as the characteristics to be optimized;
generating an optimization scenario based on the at least one improvement program; and
generating (318) one or more of the recommendations, based on the optimization scenario.
3. The system (100) as claimed in claim 2, wherein the system identifies the one or more characteristics as the characteristics to be optimized, by:
comparing the distance in characteristics value (di) of each characteristic with a threshold of distance in characteristics; and
selecting one or more characteristics for which the di value exceeds the threshold of distance in characteristics, as the characteristics to be optimized.
4. The system (100) as claimed in claim 1, wherein the optimization requirement comprises of characteristics corresponding to one or more user requirements with respect to optimization of one or more resources in the current city model.
5. A processor implemented method (200) for generating resource optimization recommendations for city planning, comprising:
colleting (202) data pertaining to a current city model, by one or more hardware processors;
collecting (204) at least one real-time input pertaining to an optimization requirement for the current city model, by the one or more hardware processors;
analyze (206) the data pertaining to the current city model based on the at least one real-time input and one or more reference city models from a reference database; and
generate (208) at least one recommendation in response to the optimization requirement collected, based on the analysis of the data pertaining to the current city model, the at least one input pertaining to the optimization requirement, and the one or more reference city models, wherein the at least one recommendation comprises of at least one optimization scenario.
6. The method as claimed in claim 5, wherein generating the at least one recommendation comprises of:
extracting (302) a plurality of characteristics from the optimization requirement;
extracting (304) a plurality of characteristics from the one or more reference city models;
identifying (306) a plurality of matches between the plurality of characteristics from the optimization requirement and the plurality of characteristics from the one or more reference city models;
listing (308) characteristics corresponding to said plurality of matches in the order of relevance, wherein the relevance is measured in terms of extent of similarity between the plurality of characteristics of the optimization requirement and the plurality of characteristics of the one or more city models;
selecting (310) a specific number of most relevant characteristics as close matches, from the characteristics listed in the order of relevance;
computing (312) a distance in characteristics value (di) for each of the most relevant characteristics;
identifying (314) one or more characteristics as characteristics to be optimized, based on corresponding di score;
generating (316) at least one improvement program for optimization of each of the characteristics identified as the characteristics to be optimized;
generating an optimization scenario based on the at least one improvement program; and
generating (318) one or more of the recommendations, based on the optimization scenario.
7. The method as claimed in claim 6, wherein identifying the one or more characteristics as the characteristics to be optimized, comprises:
comparing the distance in characteristics value (di) of each characteristic with a threshold of distance in characteristics; and
selecting one or more characteristics for which the di value exceeds the threshold of distance in characteristics, as the characteristics to be optimized.
8. The method as claimed in claim 6, wherein the optimization requirement comprises of characteristics corresponding to one or more user requirements with respect to optimization of one or more resources in the current city model.
, Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:

METHOD AND SYSTEM FOR DATA ANALYSIS FOR GENERATING RESOURCE OPTIMIZATION RECOMMENDATIONS DURING CITY PLANNING

Applicant

Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.


TECHNICAL FIELD
The disclosure herein generally relates to city planning systems, and, more particularly, to generating recommendations for city planning using a city planning system.

BACKGROUND
City/urban planning is a term that broadly refers to processes concerned with effective use of land and resources, which further involves effective planning and allocation of various resources/facilities, so as to constitute a sustainable environment for people. A few examples of the resources/facilities are transportation, healthcare, public welfare, water, infrastructure, communication networks, waste management and so on. This process further involves identification of resources/facilities that would constitute a city, and planning distribution of the resources across the available land. The city planning further involves planning the resources and resources aiming optimized use of resources with an eye on saving resources on future.
An urban planner is a person who performs urban planning. Typically during the urban planning, an urban planner collects requirements, and decides how different resources/facilities can be placed in available land to meet the requirements. A disadvantage of this ‘manual planning’ is that final result of planning (in terms of resource allocation, distribution, and so on) depends solely on experience and skills of the urban planner. As this involves a lot of data processing, the final result is likely to be affected by possible human errors. There exist certain systems that help/assist the urban planners with the urban planning. Such systems use different mechanisms to process data and provide required assistance to the users. Further, capability of the systems in terms of parameters being taken into consideration for generating the final result also varies, which in turn affects the final result.
SUMMARY
Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a system for urban planning is provided. The system includes a processing module comprising a plurality of hardware processors, one or more Input/Output (I/O) interfaces, and a memory module comprising a plurality of instructions. The plurality of instructions when executed cause at least one of the plurality of hardware processors to collect data pertaining to a current city model. Further the system collects at least one real-time input pertaining to an optimization requirement for the current city model. The system further analyzes the data pertaining to the current city model based on the at least one real-time input and data from one or more reference city models from a reference database. The system further generates at least one recommendation in response to the optimization requirement collected, based on the analysis of the data pertaining to the current city model, the at least one input pertaining to the optimization requirement, and the one or more reference city models, wherein the at least one recommendation comprises of at least one optimization scenario.
In another aspect, a processor implemented method for generating resource optimization recommendations for city planning is provided. The method involves the following steps: colleting data pertaining to a current city model, by one or more hardware processors; collecting at least one real-time input pertaining to an optimization requirement for the current city model, by the one or more hardware processors; analyzing the data pertaining to the current city model based on the at least one real-time input and one or more reference city models from a reference database; and generating at least one recommendation in response to the optimization requirement collected, based on the analysis of the data pertaining to the current city model, the at least one input pertaining to the optimization requirement, and the one or more reference city models, wherein the at least one recommendation comprises of at least one optimization scenario.
In yet another aspect, a non-transitory computer readable medium for generating resource optimization recommendations for city planning is provided. The method involves the following steps: colleting data pertaining to a current city model, by one or more hardware processors; collecting at least one real-time input pertaining to an optimization requirement for the current city model, by the one or more hardware processors; analyze the data pertaining to the current city model based on the at least one real-time input and one or more reference city models from a reference database; and generate at least one recommendation in response to the optimization requirement collected, based on the analysis of the data pertaining to the current city model, the at least one input pertaining to the optimization requirement, and the one or more reference city models, wherein the at least one recommendation comprises of at least one optimization scenario.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
FIG. 1 illustrates an exemplary system for providing resource optimization recommendations, according to some embodiments of the present disclosure.
FIG. 2 is a flow diagram depicting steps involved in the process of generating recommendations for resource optimizations for city planning, using the system of FIG. 1, according to some embodiments of the present disclosure.
FIG. 3 is a flow diagram depicting steps involved in the process of performing data analysis for generating recommendations for resource optimizations, using the system of FIG. 1, according to some embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope and spirit being indicated by the following claims.
Referring now to the drawings, and more particularly to FIG. 1 through FIG. 3, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
FIG. 1 illustrates an exemplary system for providing resource optimization recommendations, according to some embodiments of the present disclosure. In an embodiment, the system 100 includes one or more processors 104, communication interface device(s) or input/output (I/O) interface(s) 106, and one or more data storage devices or memory 102 operatively coupled to the one or more processors 104. The one or more processors 104 that are hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, graphics controllers, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) are configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud and the like.
The I/O interface device(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server.
The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, one or more modules (not shown) of the system 100 can be stored in the memory 102.
In an embodiment, the system 100 includes one or more data storage devices or memory 102 operatively coupled to the one or more processors 104 and is configured to store instructions configured for execution of steps of the method 200 by the one or more processors 104.
The system 100 is configured to generate one or more recommendations to city planning, by processing real-time inputs as well as one or more reference data stored in an associated reference database. The process is explained below:
In order to generate the one or more city planning recommendations, the system 100 receives a ‘current city model’ as input. The current city model represents current model of a city (which is to be developed/planned further), which further includes data pertaining to existing resources and further details of the existing resources. For example, consider that ‘transport’ is one of the existing resources. The current city model can then specify what types of transports (for example, road, rail, waterways) exist in the city, which all parts of the city are connected via one or more modes of transport, which areas of the city are not connected yet, and so on. In an embodiment, the current city model is fed as input to the system 100. In another embodiment, the current city model is generated by the system 100, based on one or more inputs pertaining to the current city model, captured from a user.
The system 100 then collects real-time input pertaining to an optimization requirement for the current city model, from a user. The optimization requirement may be specific to developing one or more specific resources in the current city model or may be specific to development of the overall city with an intent. For example, the optimization requirement may specify a ‘goal’ in terms of development ‘transport system’ in the city. In another example, the optimization requirement may specify that the whole city model is to be developed further with the intent of converting the city to a ‘smart city’.
The system 100 maintains in an associated database (maybe in the memory module 102), one or more reference databases, wherein the one or more reference databases contain information pertaining to one or more reference city models. Each reference city model may contain information pertaining to resources that form the city, the manner in which each resource is planned/arranged and so on, and corresponding characteristics. The data may be arranged in characteristics ? factor ? indicator format. If transportation is considered, data may be arranged as: Transportation ? Coverage ? a. Road network length (in kilometers), b. number of busses, 3. Quality of busses (poor, good, excellent). For the indicators ‘road network length’ and ‘number of buses’, di is a numeric difference whereas the indicator ‘quality of buses’ being a categorical value the value of di is calculated based on certain value attached to each category. For instance, 20 for poor, 60 for good, 100 for excellent and so on.
In addition to this, certain reference city models may be tagged as being planned with a specific intent. For example, one of the reference city models may be that of a ‘smart city’. Tagging that reference city model as ‘smart city’, allows the system 100 to select this reference city mode as reference if recommendations are to be generated to convert a city to a smart city. In each reference city model, data is indexed and tagged to help the system 100 identify appropriate reference city models and data.
In response to the optimization requirement received, the system 100 searches in the reference database and identifies one or more reference city models that match the current city models and the ‘goal’ or ‘requirement’ specified in the optimization requirement. Based on the identified one or more reference city models, the system 100 generates one or more recommendations to the user. Details of process executed by the system 100to generate the recommendation(s) is provided with description of FIG. 3. At this stage the system 100 identifies one or more action items to be executed so as to meet the ‘goal/requirement’. The system 100 may be further configured to simulate the identified action items and display to the user how the city will look like if the identified action items are executed. The system 100 may further provide options for the user to accept or discard one or more of the action-items (i.e. the recommendations). Further, based on action taken by the user (i.e. accepting or rejecting the one or more recommendations), the system 100 takes feedback, which may be further used by the system 100 to fine-tune/refine the recommendations generated.
A few examples of the recommendations generated by the system are:
Transport
Implement and Improve Public Transport Network. More city buses, rail network, local taxis, share taxis, and so on.
Road Safety: Traffic Signals, one-ways, slow drive zone, foot over bridges, dedicated parking zone, and so on
Living:
Clean Energy:
Clean Water: Improve Access to water supply, Implement Piped water Network, Revive Lakes/reservoirs, and so on.
Budget Accommodation: Build holiday homes, tourist home stay
Recreation: Convert landscape, parks into amusement parks, theatres, roadshow
Health: Improve healthcare facilities in city zones by population.
FIG. 2 is a flow diagram depicting steps involved in the process of generating recommendations for resource optimizations for city planning, using the system of FIG. 1, according to some embodiments of the present disclosure. The system 100, through appropriate user interfaces provided to different users of the system, collects (202) data pertaining to a current city model as one of the inputs. The data pertaining to the current city model further comprises of data pertaining to parameters such as but not limited to total land area, a spatial map, population, resources currently available, resource deployment across the land, and so on. The system 100 also collects real-time data using an associated edge analytics system that deploys sensors and such data collection tools so as to capture real-time inputs. For example, using sensors for weather sensing, the system 100 collects real-time weather data, which in turn can be used for generating recommendations accordingly. The system can collect data pertaining to preferences/requirements with respect to city planning, from different users, as input. The system 100 can further collect data pertaining to usage, availability, and so on pertaining to different resources that constitute the current city model, from corresponding city authorities, by providing appropriate user interfaces and communication channels. The system 100 can further collect one or more data that is to be stored in a reference database maintained in the system 100.
In addition to the current city model, the system 100 collects (204) at least one real-time input pertaining to an optimization requirement with respect to the current city model. Optimization in the context of functionalities of the system 100 refers to a request to optimize usage of one or more resources in the current city model, with a specific goal/target. For example, the goal/target in terms of optimizing the water resources in the city may be reducing water wastage. In various embodiments, the optimization may involve one or more of addition of new resources, removal of existing resources, restructuring existing resources or resource usage, expanding reach/coverage of one or more resources and so on.
The system 100 can be configured to perform data conversions for the data collected in a) Printed form [OCR and Contextual interpretation], b) Photogrammetry [Topography/geographical spread], c) Videogrammetry [slow changing/ Movement], and/or d) any-to-any conversion of machine readable formats.
In case of data unavailability or partially missing data for a particular resource, the system 100 performs data approximation, whereby system takes one form of data and creates target data with the help of reference data. For example, a city has road layout available but the drainage network needs to be approximated for it unavailability. The system 100 takes reference data (road layout, in this example) from one or more reference city models and ordinates and offsets for approximating drainage or water network. Ordinates and offsets for drainage data approximation could be of following nature: a) drain with is 20% of road width b) on the right side of road and c) with 10% offset off the road.
The system 100 stores in the memory module 101, at least one reference database which contains information pertaining to reference city models, classification of the reference city models according to purpose/preferences, characteristics of each of the reference city models, sub-attributes of each of the characteristics and so on. In response to the optimization request received, the system 100 analyzes (206) the data pertaining to the current city model, based on the data from the reference database, and generates (208) at least one recommendation for resource optimization, which in turn helps in city/urban planning. The system 100 then provides the generated recommendations to the user.
FIG. 3 is a flow diagram depicting steps involved in the process of performing data analysis for generating recommendations for resource optimizations, using the system of FIG. 1, according to some embodiments of the present disclosure. During the data analysis for generating recommendations, the system 100 extracts (302) a plurality of characteristics of the current city model, and further identifies and extracts sub-attributes of each of the characteristics. The system 100 further extracts (304) a plurality of characteristics of one or more of reference city models stored in the reference database, and further identifies and extracts sub-attributes of each of the characteristics. Further, by comparing the extracted characteristics and the sub-attributes (of the current city model and the one or more reference city models), the system 100 identifies (306) one or more matching characteristics between the current city model and that of the at least one reference city model. After identifying matches, the system 100 lists (308) the matching characteristics from the at least one reference city model in the order of relevance, wherein the relevance is identified based on extent of similarity (based on pre-configured or dynamically configured values) between the characteristics of the current and reference city models. Further, from the list of matching characteristics, a certain number of most relevant characteristics (i.e. certain number of characteristics from top of the list, if the characteristics are listed in descending order of relevance) are selected (310) as close matches. For example, consider that the system 100 identifies from one or more reference city models, 5 characteristics (a, b, c, d, e) as the matching characteristics. Out of the 5 characteristics, some (for example, a and d) may be close matches, whereas the rest (i.e. a, c, e) may not be close matches.
Further, for each characteristic identified as the close match, the system 100 computes (312) a ‘distance in characteristics value (di)’. The value of di is calculated as:
di = v(2&?_(j=1)^n¦(w_j d_ij )^2 ) --- (1)
where dij is normalized numerical difference at indicator level and wj is a weight attached to that indicator
To understand the term ‘indicator’, consider the following example. Any resource specific data is arranged in “characteristics ? factor ? indicator” format.
Further, based on the value of di computed for each of the characteristics, the system 100 identifies (314) one or more characteristics (among the characteristics identified as close matches) as characteristics to be optimized. Here di for each characteristic may be a Euclidean distance, and represents extent to which the characteristics from the current city model and the at least one reference city model differs from each other. A threshold of extent of difference may be pre-configured or dynamically configured, and if the extent of difference for a pair of current city model characteristic-reference city model characteristic exceeds the threshold, that indicates that the current city model characteristic needs to be optimized to match the reference city model characteristic, so as to meet the requirements/goals. The threshold of extent of difference may be defined in terms of a percentage value (for example, 40%), and if the extent of difference exceeds the set threshold of extent of difference, that indicates that the corresponding characteristics are to be optimized. After identifying the characteristic(s) to be optimized, the system 100 further generates (316) at least one improvement program for optimizing the one or more characteristics identified as to be optimized. The ‘improvement program’ may specify at least the following: 1. which all resources are to be optimized, 2. locations in the current city model where the resources can be optimized, 3. to what extent each resource is to be optimized, 4. resource(s) to be added, 5. resource(s) to be removed, and so on. The improvement program may form an optimization scenario, and accordingly generates (318) at least one recommendation in response to the optimization request received from the user. In an embodiment, the system 100 depicts/illustrates effect of the optimization scenario if the recommendations are implemented. In this step, the system 100 may overlay a location map of area covered by the current city model, with the changes matching the action items identified, and may display this to a user. This may help the user visualize an ‘improved city’ even before actually implementing the changes, and may further help the user take appropriate actions (such as accepting or rejecting one or more of the recommendations made by the system 100).
In addition to the recommendations made, the system 100 may further detect and convey to the users, impact of change(s) corresponding to the recommendation(s) made. Data pertaining to such impacts may be fetched based on data in the one or more reference models considered.1. The impact may be categorized into 3 types: 1. environmental impact, 2. social impact, and 3. business impact.
Environmental impact: It highlights the environmental impact by reporting the change (reduction/increase) of land area, green space, pollution by generating a predictive analytic model using any known technique.
Social impact: The system 100 also highlights how the citizens will be impacted, how they will be benefited with the implementation of the action items, and what initial inconvenience they may have to face.
Business impact: Which are the government authorities who can possible fund for the implementation, Public partnership/sponsors suggestion etc.
In addition to generating the recommendations, the system 100 can be further used to predict future demand of a resource. For this purpose, the system 100 considers changing characteristics and attributes (such as but not limited to population, city expansion and so on), and data pertaining to consumption of resources (may be collected in a zone-wise manner), and by processing this data, determines a future demand of each resource. In an embodiment, the future demand of a resource is computed as:
P_ij= (A_j^? D_ij^(-ß))/(?_(j=1)^n¦?A_j^? D_ij^(-ß) ?) --- (2)
Where:
Aj is a measure of attractiveness of resource j, and is calculated as:
A_j=k_1 FF+k_2 CF+k_3 CD--- (3)

Where FF, CF, CD are footfall, cash flow, and distance respectively from centroid of resource cluster, chosen from a set of attractiveness parameters; k_n are the weight parameters that may be obtained from empirical observations
a is an attractiveness parameter (estimated from empirical observations)
Dij is the distance from i to j, and is calculated as:
D_ij=p_1 TC+p_2 TD+k_3 CC --- (4)
TC, TD, CC are time to construct, time to destination, cost to construct of resource cluster respectively chosen from a set of distance parameters; p_n are the weight parameters obtained from empirical observations
ß is the distance decay parameter (estimated from empirical observations)
n is the total number of facilities including facility j.
Example implementation:
Consider that the system 100 is used to help a city planner (a user) to identify action items for developing a city ‘X’ as a tourist destination. The user may be able to select/choose the intent from a set of options. For example, some of the available options for the user may be developing the city as: 1. a tourist hub, 2. an Information Technology (IT) hub, 3. a healthcare hub, 4. an educational hub, and so on. In this example scenario, the user selects the intent as ‘developing the city as a tourist destination’. The system 100 collects current city model pertaining to the city ‘X’. The current city model may contain data such as but not limited to demographic data of the land area covered by X, list and layout of resources that are currently present in the city X, and population.
The system 100 processes data from the current city model, the intent/goal/requirement, and data from one or more reference city models in a reference database, so as to identify one or more reference city models that match the current city model and the intent. At this stage the system 100 may pick all the reference city models that have been tagged as ‘tourist hub’ (or any similar tag that specifies purpose/intent with which that city has been modelled). Further the system 100 processes the data (i.e. characteristics) from the current city model and that from the one or more reference city models selected, and identifies matches. From the identified matches the system 100 selects a few characteristics as ‘close matches’. In an embodiment, the characteristics selected as the close matches are used as further reference points to identify action items, by the system 100. The system 100 then calculates a distance in characteristics value (di) between the characteristics of the current city model and the corresponding characteristics from the characteristics identified as the ‘close matches’. Based on the comparison, the system 100 identifies one or more characteristics as the characteristics to be optimized. During this process the system 100 checks and identifies the characteristics that deviate considerably (in terms of a threshold) from the close matching characteristic which act as a reference point.
In order to develop the city X as the tourist destination, resources such as transport, water networks, tourist attractions, and so on. If the comparison with the reference city models indicate that the water network in the city is not well developed (in terms of the distance in characteristics value as compared to the one or more reference city models), then accordingly suggestions/recommendations are made by the system 100 to help the user develop the city to satisfy the intent.
The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
The embodiments of present disclosure herein addresses unresolved problem of city planning. The embodiment, thus provides a mechanism to generate recommendations for resource optimization, as part of city planning.
It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.
The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
It is intended that the disclosure and examples be considered as exemplary only, with a true scope and spirit of disclosed embodiments being indicated by the following claims.